• 　pdf file print out
  • Wavelets, approximation and statistical applications
  • Normal Linear regression models by Michael
• 統計的漸近理論
  • <全章[pdf file]
  • １章
  • ２章
• セミパラの講義録
• 経験過程の講義録
• 特論の講義録
• 統計統計講義録
• 統計演習問題
• 統計演習問題の講義録
• Berger and Casella's solution manuals
• tsukuma's paper
• Tsukuma's thesis
• Fujita's master thesis

Top     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

人の pdf ファイル

• Chen, Hung
  • Asymptotically efficient estimation in semiparametric generalized linear model, AS 23(1995), 1102-1129
• Haff, L.R.
  • The variational form of certain Bayes estimaotors, AS 29(1991), 1163-1190
• Averkamp, R. and Houdre, C.
  • Wavelet thresholding for non necessarily Gaussian noise: Idealism
• Chen, Zhiqiang and Tyler, D.E.
  • The influence function and maximum bias of Tukey's median, AS, 2001
• Johnstone, Iain
  • Function estimation in Gaussian noise: sequence models
• Lai, T.L. and Robbins, Herbert
  • Adaptive design and stochastic approximation, AS(1979), 1196-1221
• Ichikawa, Masanori
  • Asymptotic expansions and bootstrap approximations in Factor Analysis, JMA(2001)
• Cardot Herve
  • Spatially adaptive spline for statistical linear inverse problems, JMA(2001)
• Van Aelst, Stefan, Rousseeuw, Peter J., Huber, Mia, and Struyf, Anja
  • The deepest regression method, JMA(2001)
• Masse, J.-C.
  • Asymptotics for the Tukey median, JMA
• Kubokawa, T. and Srivastava, M.S.
  • Estimating risk and the mean squared error matirx in Stein estimation
• Manzoli, A. and Perez, F.J.
  • A statistics for testing the null-hypothesis of elliptical symmetry, JMA
• Cuadras, C.M.
  • On the covariance between functions, JMA
• Taneichi, N., Sekiya, Yuri, and Suzuki
  • Asymptotic approzimations for the distributions of the multinomial goodness-of-fit statistics under local alternatives, JMA
• Wang, Qi-Hua
  • Empirical likelihood semiparametric regresson analysis under random censorship, JMA
• Zhang, Biao
  • assessing goodness-of-fit of generalized logit models based on case-control data, JMA
• Fang, Hong-Bin, Fang, Kai-Tai, and Kotz, S.
  • The meta-elliptical distributions with given marignals, JMA
• Lai, T.L. and Wei, C.Z.
  • Least squares estimates in stochastic regression methods with applications to identification and control of dynamic systems, AS 10(1982), 154-166.
• Gupta, Rameshwar D. and Richards, Donald St. P.
  • Moment properties of the multivariate Dirichlet distributions, JMA
• Hoff, Peter D.
  • Nonparametric estimation of convex models via mixtures
  • Constrained nonparametric estimation via mixtures
• Akritas, Michael G. and Van Keilegom, I.
  • Non-parametric estimation of the residual distribution, Scandinavian J. of Staitsics vol. 28(2001), 549-567.
• Ghosh, D.
  • Efficiency consideratins in the additive hazards modell with current status data, Statistica Neerlandica vol. 55(2001), 367-376.
• Li, Bing
  • A minimax approcah to consistency and efficiency for estimating equations, AS, 24(1996) 1283-1297
  • A derivatrion function for the quasi-likelihood method, Biometrika 80(1993), 741-753.
  • Potential functions and conservative estimating funcitons with McCullah, Peter, AS vol 22(1994), 340-356
• Wong, Wing Huag
  • Theory of partial likelihood, AS 14(1986), 88-123.
• Mcleish, D.L. and Small,C.G.
  • A projected likelihood function for semiparametric models, Biometrika, vol 79(1992), 93-102.
• Wedderburn, R.W.M.
  • Quasi-likelihood funcitons, generalized linear models, and the Gauss-Newton method, Biometrika 61(1974), 439-447.

A     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Andersson and Perlman
  • some papers related to 93 Annals' paper
  • andersson and Madesn AS (1998), no.2.
  • Massam
  • Anderson and Perlman, JMA (1998)
  • Thomas' AS paper
• Andra's Antos
  • Performance limits of nonparametric estimators Ph.D. Thesis, Technical University of Budapest, Budapest, May 1999.
  • Strong minimax lower bounds for learning with G. Lugosi
  • Strong minimax lower bounds for learning Machine Learning 30:31-56, 1998. Economics Working Paper 197, Universitat Pompeu Fabra, 08005 Barcelona, January 1997
  • Lower bounds for Bayes error estimation IEEE Transactions on Pattern Analysis and Machine Intelligence, 21:643-645, 1999.
  • Lower bounds on the rate of convergence of nonparametric regression estimates with L. Gyorfi and M. Kohler, Journal of Statistical Planning and Inference, 83:91-100, 2000.
  • Lower bounds for the rate of convergence in nonparametric pattern recognition Theoretical Computer Science, XX:YYY-ZZZ, 2001
• Arcones
  • M -estimator paper
  • On the asyptotic accureacy of the bootstrap under arbitrary resampling size
  • The Bahadur-Kiefer representation of the two dimenstional spatial median, AISM, vol 50(1998)
  • The large deviation priciple of stochastic processes
  • The large deviation priciple for empirical processes
  • Large and monderate deviations of empirical processes with nonstandard rates
  • Moderate deviations fro M-estimators
  • Weak convergence for the row sums of a traingular array of empirical processes indexed by a manageable triangular array of functions
  • Weak convergence of convex stochastic processes, Statistics & Probability letters vol. 37(1998), 171-182.
  • Asymptotic distribution of regression M-estimators
  • Second order represenatations of the least absolute deviation regression estimator, AISM vol. 50(1998), 87-117.
  • Convergence of the best M-estimator over a parametric family of M-estimators
• Andrews, Donald W.K.
  • Estimation when a parameter is on a Boundary: Theory and applications
  • Testing when a parameter is on the Boundary of the maintained hypothesis
  • Hypothesis testing with a restricted parameter space
  • Higher-order improvements of a computationally attractive k-step Bootstrap for extremum estimators
  • Consistent model and moment selection criterion for GMM estimation with application to Dyanamic panel data models
  • Equvalence of the higher-order asymptotic efficiency of k-step and extremum statistics
  • An empirical processes CLT for dependent non-identically distributed random variables
  • An introduction to Econometric applications of empirical process theory for dependent random variables
  • An introdcution to FCLT for dependent stochastic processes with Pollard, D.
  • Asymptotic for semiparametric Econometric models via stochastic equicontinuity, Econometrica, vol. 62, 43-72.
  • Semiparametric estimation of a sample selection model
  • Consistent model and moment selectin criteria for GMM estimation with application to dymamic panel data models
  • Equivalence of the higher-order asymptotic efficiency of k-step and extremum statistics
  • Consistent moment selection procedures for generalized method of moments estimation
  • A conditional Kolmogorov test
  • The large sample correspondance between classical hypothesis tests and Bayesian posterior odds tests
  • Tests of specification for parametric and semiparametric models
  • Asymptotic normality of series estimators for nonparametric and semiparametric regression models
  • Consistency in nonlinear Econometric models: a generic uniform law of large numbers
  • Asymptotic results for generalized Wald tests
  • Chi-square diagostic tests for Econometric models: theory
  • A note on the unbiasedness of feasible GLS, quasi-maxmimum likelihood, robust, adaptive, and spectral estimaotrs of the linear model
  • Robust estimation of loacation in a Gaussian parameter model
  • Stability comparisons of estimators, Econometrica, vol. 54, 1207-1235.
  • LLN for dependent non-identically distributed random variables
  • Generic uniform convergence

B     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Mouli Banerjee
  • Dissertation: Likelihood ratio inference in regular and non-regular problems
  • Lecture note
• Barbour
  • Logarithmic combinatorial structures
• Barrera
  • Bootstraping robust regression
  • Uniform Asymptotic for robust location
  • thesis
• Bass, R.
  • 講義録 (2002 年 7 月編集
  • 講義録 (undergraduate)
  • 講義録
• Basu ( disparity)
  • Robustification of the MLE without loss of efficiency by Chakraborth, B., Basu, and Sakar, S.
  • A comparison of related density-based MD estimators by Jones, M.C., Hjort, N.L., and Basu
  • Minimun Hellinger distance estimates for pjarametric model by Rudolf Beran
  • Efficiency versus robustness: the case of MHD and related methods by Bruce Lindsay
  • Minimum Negative Exponential disparity estimaation in parametric model by Basu, Sarker, S. and Vidyashaankar, A.N.
  • Weighted likelihood estimating equations: the discrete case with application to logistic regression by Markatou, M., Basu, and LIndsay, B.
  • Minimum Hellinger distance estimation for the analysis for cound dat by Simpson, D.G.
  • Helinger deviation test by Simpson, D.G.
  • MHD estimation for multivariate location and covariance by Tamura, R.N. and Boos, D.D.
  • Do robust estimators work with real data? by Stigler, S.M.
  • Efficient estimates and optimum inference procedures in large samples by Rao, C.R.
  • M-estimation for discrete data: asymptotic distribution theory and implication by Simpsom, D.G., Carroll, R.J., and Ruppert, D., AS 15(1987), 657-669
  • Optimally bounded score functions for generalized linear models with application to logistic regression by Stefanski, L.A., Carroll, R.J., and Ruppert, D., Biometrika 73(1986), 413-424.
  • Iteratively reweighted least squares for mle, and some robust and resistant alternatives by Green, P.J., JRSS B 46(1884), 149-192.
  • Least median of weighted squares in logistic regressin with large strata by Christmann, A., Biometrika 81(1994), 413-417.
  • Binary regression models for contaminated data by Copas, J.B., JRSS B 50(1988), 225-265
  • On robustness in the logistic regression model by Carroll, R.J. and Pederson, S. JRSS B 55(1993), 693-706.
  • Consistency for deviance-based M-estimators by Lenth, R.V. and Green, P.J., JRSS B 49(1987), 326-330.
• Beran, R.
  • Lucien Le Cam: An application
  • Superefficient estimation of multivariate trend
  • REACT trend estimation in correlated noise
  • Hyprid shirinkage estimation using penalty bases for the one-way layout
  • External fits in REACT confidence sets
  • ASP algorithsms for denoising two-way layouts
  • The impact of the bootstrap on statistical algorithms and theory
  • Improving penalized least squares thorough adaptive selection of penalty and shrinkage
  • REACT scatterplot smoothers: superefficiency through basis economy
  • Hybird shrinkage estimators using penalty bases for the one-way layout
  • Nonparametric regession with randomly censored survival data
  • Adaptive estimation of directional trend
  • ASP Fits to Multi-way Layouts
• Breiman, Leo.
  • Bagging predictors
  • Arcing classifiers
  • Arcing the edge
  • Predicting games and arcing algorithems
  • Using convex pseudo-data to increase prediction accuracy
  • Randomizing outputs to increase prediction accuracy
  • Half&Half bagging and hard boundary points
  • Using adaptive bagging to debias regressions
  • Some infinity theory for predictor ensembles
  • Random forests
• Breiman and Friedman
  • Predicting multivariate responses in multiple linear regression. With discussion and a reply by the authors. J. Roy. Statist. Soc. Ser. B 59 (1997), no. 1, 3--54.
  • Unified papers
• Bickel
  • Unified papers
  • Calculus of statistical efficiency in a general setting ( Thesis by Jaimyoug Kwon, 2000 Fall
  • Inference for semiparamteric models by Bickel and Kwon
• 　Birge and Massart
  • Unified papers
  • Minimum constrast estimators on seive
  • Risk bounds for model selection via penalization with Andrew Barron
  • From model selection to adaptive estimation
  • Gaussian model selection
  • A generalized C_p criterion for Gaussian model selection
  • Concentration inequalities using the entropy method with S. Boucheron, Lugosi G., and Pascal Massart
• Bose Arup
  • Asymptotic Distribution of the Kaplan-Meier U-Statistics with Arusharka Sen, JMA pp. 84-123
  • Dimension asymptotics for generalised bootstrap in linear regression with Arup Bose, AISM
  • Comparison of bootstrap and jackknife variance estimators in linear regression: second order results with Arup Bose, Sinica
  • Second order comparison of different bootstrap and jackknife methods in regression with Arup Bose
  • Second order comparison of different bootstrap and jackknife methods in multiple regression with Arup Bose
  • Generalised bootstrap for estimating equations with Arup Bose
  • Generalised bootstrap for estimators of minimisers of convex functionals with Arup Bose
• Brown, L.D.
  • The Analogy Between Statistical Equivalence and Stochastic Strong Limit Theorems, 2002
  • Racetrack Betting and Consensus of Subjective Probabilities with Y. Lin, 2002
  • Root-Unroot Methods for Nonparametric Density Estimation And Poisson random-effects models with R. Zhang and L. Zhao, 2002 OHP
• University of Bristol
  • Stuart Coles
    • Linear models and experimental design
    • Comuter intensive methods
    • Notes on extreme value theory
  • Green, P.
    • Decomposable graphical Gaussian model determination with Giudoch, P.
    • HIdden Markov models and disease mapping with Richardon, S.
    • Bayesian growth curves using normal mixtures withnonparametri weights with Scaocia, L.
    • Bayesian analysis of Possion mixtures with Viallefont, V. and Richardson, S.
    • Modelling heterogeneity with and without the Dirichlet process with Richardson, S.
    • Modelling spatially correlated data via mixutes: Bayesian approach with Fernandex, C.
    • Bayesina variable selection and the Sendsen-Wang algorithm with Nott, D.J.
    • Delayed rejection in reversible jump Metropolis-Hastings with Mira, A.
  • Bernard Silverman
    • Empirical Bayes selectin of wavelets thresholds with I.M. Johnstone
    • Risk bounds for empirical Bayes estimates fo sparse sequences with I.M. Johnstone
    • Wavelet for regression and other statistical problems with Nason, G.P.
    • Posterior probability intervals for wavelet thesholding with Barber, S. and Nason, G.P.
    • Some new methods for wavelet density estimation with Herrick, D.R.M.
    • Incorporating information on neighboring coefficients into wavelet estimation with Tony Cai
    • A wavelet mixture approach to estimation of image deformation functions with T.R. Downie
    • Wavelet in Statistics: some recent developments
    • The discret multiple wavelet transform and thresholding methods with T.R. Downie
    • Wavelet in Statistics: beyond the standard assuptions
    • A wavelet based approach to deformable templates with T.R. Downie, L. Shepstone
    • Wavelet therhold estimate for data with correlated noise with I.M. Johnstone
    • Stochastic expansion in an overcomplete wavelet dictionary with F. Abramovich, and T. Spantinas
    • Extending the scope of wavelet regression methods by coeeficient-dependent thresholding with A. Kovac
• Bunea, F.
  • Penalty choices and consistent covariate selection in semiparametric regression
  • A model selection approach to semiparametric regression
  • Two-stage model selection procedures in partially linear regression with Wegkamp, M.
  • Covariate selection for semiparametric hazard fucntion regression models McKeague, I.W.

C     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Tony Cai
  • Lecture on wavelets
  • Papers
  • Buja's paper on boosting
  • Jianhua Huang
  • Linda Zhao's papers
  • Rates of convergence and adaptation over Besov spaces under pointwise risk
  • Confidence intervals for a binomial proportion and asymptotic expansions with Brown, L.D., and DasGupta, A.
  • Interval estimation for binomial proprotion with Brown, L.D. and A. DasGupta
  • On adaptive wavelet estimation of a derivative and other related linear inverse problems
  • Adaptive wavelet estimation a block thesholding and Oracle inequality approach
  • On block thresholding in wavelet regression: adptivity, block size, and threshold level
  • Asymptotic equivalence theory for nonparametric regression with random noise with Brown, L.D. and Low, M.G., and Zhang, C.H.
  • Interval estimation in exponential families with Brown, L.D. and DasGupta, A.
  • On adaptability and information pooling in nonparametric function estimation
  • Tradeoffs between global and local risks in nonparametric function estimation with Low, M.G. and Zhao, L.H.
  • A note on nonparametric estimation of linear functionals with Low, M.G.
  • Minimax estimation of linear functionals over nonconvex parameter spaces with Low, M.G.
  • Incorporating information on neighboring coefficients into wavelet estimation with Silverman, B.W.
  • Wavelet shrinkage for nonequispaced samples with Brown, L.D.
  • Wavelet estimation for samples with random uniform design with Brown, L.D.
  • Minimax estimation of linear functionals over nonconvex parameter spaces with Mark G. Loh, to appear in AS
  • An adaptation theory for nonparametric confidence intervals with M. Loh
  • Nonparametric function estimation over shrinking neighborhoods: Superefficiency and adaptation with M. Loh
  • On modulus of continuity and adaptability in nonparametric functional estimation with M. Loh
• Cambridge University
  • Lecture notes on Optimization
  • Lecture notes on Information Theory
  • Lecture notes on Algebratic Coding Theory
• 　Cambridge University
  • Lecture notes on Optimization
  • Lecture notes on Information Theory
  • Lecture notes on Algebratic Coding Theory
• Canadain Journal of Statistics
  • December 2002
    • A glimpse of Pa'l Erdos's impact on probability-statistics by Miklo's Csorgo
    • Nonparametric estimation of the bivariate CDF for arbitrarily censored data by Robert Gentleman & Alain C. Vandal
    • Estimating the order of a hidden Markov model by Rachel J. MacKay
    • Testing circular symmetry by Arthur Pewsey
    • Robust sequential designs for nonlinear regression by Sanjoy Sinha & Douglas P. Wiens
    • Penalized likelihood regression: General formulation and efficient approximation by Chong Gu & Young-Ju Kim
  • 2003 March
    • A fast distance-based approach for determining the number of components in mixtures by Sujit K. Sahu & Russell C. H. Cheng
    • Simple estimation of the mode of a multivariate density by Christophe Abraham, Ge'rard Biau & Benoi^t Cadre
    • On Blest's measure of rank correlation by Christian Genest & Jean-Franc,ois Plante
• Carnegie Mellon University
  • Schervish at Canegie Mellon
    • Lecture notes on Statistics
    • Exercised
  • Wasserman, L.
    • Uniform Consistency in caual inference by Robins, J.M., Scheines, R., Spirtes,P. and Wasserman, L.
    • Rates of convergence of posterior distribution by Shen, X. and Wasserman, L.
    • The consistency of posterio distributions in nonparametric problems by Barron, A., Schervish, M.J. and Wasserman, L.
    • Rates of convergence for the Gaussian mixture sieves by Genovese, C. and Wasserman, L.
    • Consistency of Bernstein polynomial posteriors by Petrone, S. and Wasserman, L.
    • Bayesian model selection and model averaging by Wasserman, L.
    • Bayesian model selection: Analysis of survival model with a surviving fraction by Seltman, H., Greenhouse, J. and Wasserman, L.
    • The foundations of statistics: a vignette by Robins, J. and Wasserman, L.
    • Confidence Sets for Nonparametric Wavelet Regression with Christopher R. Genovese
    • False Discovery Rates with Christopher Genovese
    • A Non-parametric Analysis of the CMB Power Spectrum with Christopher J. Miller, Robert C. Nichol, Christopher Genovese
    • A new source detection algorithm using FDR with A.M. Hopkins, C.J. Miller, A.J. Connolly, C. Genovese, R.C. Nichol
    • Nonparametric Inference in Astrophysics with Woncheol Jang, Chris Miller, Andy Connolly, Jeff Schneider, Chris Genovese, Bob Nichol, Andrew Moore
    • Genomic control, a new approach to genetic-based association studies with B. Devlin, Kathryn Roeder
    • Controlling the False Discover Rate in Astrophysical Data Analysis with Christopher J. Miller, Christopher Genovese, Robert C. Nichol, Larry Wasserman, Andrew Connolly, Daniel Reichart, Andrew Hopkins, Jeff Schneider and Andrew Moore
    • Outlier Detection and False Discovery Rates for Whole-genome DNA Matching with Tzeng, Jung-Ying, Byerley, W., Devlin, B., Roeder, Kathryn
    • Iterative Markov Chain Monte Carlo Computation of Reference Priors and Minimax Risk with John Lafferty
    • Operating Characteristics and Extensions of the FDR Procedure with Christopher Genovese
    • Fast Algorithms and Efficient Statistics: Density Estimation in Large Astronomical Datasets with R.C. Nichol, A.J. Connolly, A.W. Moore, J. Schneider, C. Genovese
    • Genomic control for association studies: A semiparametric test to detect excess-haplotype sharing with B. Devlin, Kathryn Roeder
    • Asymptotic Inference for Mixture Models Using Data Dependent Priors
  • Shrinkage estimators for covariance matrices by Daniels, M. and Kass, R.E.
  • Methods and criterion for model selection by Kadane, J.B. and Lazar, N.A.
  • The Schwarz criterion and related methods for model selection in linear regression by Pauler, D.
  • NOnconjugate Bayesian estimation of covariance matrices and its use in Hierarchical models by Daniels, M. and Kass, R.E.
  • A general predictive criterion for model selection by Trottini,M.and Spezzaferi, F.
  • On the calibration of Bayesian model choice creiterion by Vlachos, P.K. and Gelfand, A.E.
  • Working with Random Systems Genovese, Christopher
• Carroll, Raymond J. at Texas A&M University
  • paper to be out with David Ruppert at Cornell University
• Olivier Catoni at Paris 6
  • Saint-Flour lecture notes (July 2001) on statiscial learning theory and stochastic optimization.
• Depth function and quantlie regression
  • Asymptotic distributions of the maximal depth estimators for regression and multivariate location . by Zhi-Dong Bai , Xuming He ; AS(2000). 1616 -1637
  • Distribution-Function-Based Bivariate Quantiles by L.-A. Chen, A. H. Welsh, JMA(2002) pp. 208-231
  • On multivariate quantile regression by Chakarborthy, B., JSPI(2003), 109-132.
• Chatterjee, S.
  • Dimension asymptotics for generalised bootstrap in linear regression with Arup Bose, AISM
  • Comparison of bootstrap and jackknife variance estimators in linear regression: second order results with Arup Bose, Sinica
  • Second order comparison of different bootstrap and jackknife methods in regression with Arup Bose
  • Second order comparison of different bootstrap and jackknife methods in multiple regression with Arup Bose
  • Generalised bootstrap for estimating equations with Arup Bose
  • Generalised bootstrap for estimators of minimisers of convex functionals with Arup Bose
  • On bandwidth selection of non-parametric kernel density estimators,
  • Parameter estimation in conditional heteroscedastic models, With Samarjit Das
• Chung (Yale Univ.)
  • Lecture notes on Stochastic Processes
  • Tutorial on SVM found in his page.

D     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• • Misspecifed model and consistency
• Dervyoe
  • Asymptotic performance bounds for the kernel estimate
  • The double kernel method in density estimation
  • ON the impossibility of estimating densities in the extermal tails with Jan Beirhant
  • A comparison of kernel density estimates with Alain Berlinent
  • Another proof of slow convergence result of Birge
  • The consistency of a smoothed minimum distance estimate with Ricardo Cao
  • The Hilbert kernel regression estimate with L. Gyoril and A. Krzyzak
  • On the Hilbert kernel density estimate with A. Krzyzak
  • New multivariate product density estimators with A. Krzyzak
  • Nonasymptotic universal smoothing factors, kernle complexity and Yatracos class with Gabor Lugosi
  • A note on the usefulness of superkenels in density esitmation
  • Universla smoothing factor, selection in density estimation: theory and practice
  • A univerally acceptable smoothing factor for kernel density estimates with Lugosi
  • A univeral lower bound for the kernel estimate
  • On the effect of density shape on the performance of its kernel estimate with Mathew, P. Wand
• Donoho, G.
  • The Kolomogorov smampler, 2002
  • Data compression and Harmonic analysis with Vetterli, M, DeVore, R.A. and Daubechies, I., 1998
  • Spare components of images and optimal atomie deocompositions, 2000(rev)
  • Ridgelets: theory and application by Emamanuel Jean Candes(dissertation), 1998
  • Orthogonaly ridgelets and linear singularities
  • Ridge functions and orthogonal ridgelets
  • PNAS inaugurd article: thight frames of k-plane ridgelts ans the problem of representating objects which are smooth away from d-dimensional sigularities in R^n
  • Curvelets- a suprising effective nonadaptive representation for objets with edges with Candes, E.J.
  • Digital curvelet transformation: strategy, implementatin and experiments with Duncan, M., 1999
  • Wegeletgs: nearly-minimax estimation of edges, 1997
  • Atomic decomposition by Bases pursuit with Chen, S.S., Saunders, M.
  • MInimax entropy and the nearly black oject with Johnstone, I.M., Hoc, J.C. and Stern, A.S., 1992
  • Minimax risk over hyoperrectangles and implication wtih Liu, R.C. and MacGibbon, B., 1990, AS.
  • Minimax risk over l_p-Balls for l_q-error with Johnstone, I.M., 1994.
  • De-noising by soft-thresholding
  • Digital Implementation of Ridgelet Packets with Ana Georgina Flesia, Hagit Hel-Or, Amir Averbuch, Raphy Coifman, and Emmanuel Candes
  • Fast X-Ray and Beamlet Transforms for Three-D Data with Ofer Levi
  • Beamlets and Multiscale Image Processing with Xiaoming Huo
  • Can Recent Developments in Harmonic Analysis `Explain' the Recent Findings in Natural Scene Statistics? with Ana Georgina Flesia
  • Fast Slant Stack: A notion of Radon Transform for data on a Cartesian grid which is Rapidly Computable,Algebraically Exact, Geometrically Faithful, and Invertible with A. Averbuch, R. Coifman, M. Israeli, and J. Walden
  • Counting Bits with Kolmogorov and Shannon
  • Adapting to Unknown Sparsity by Controlling the False Discovery Rate with Felix Abramovich, Yoav Benjamini, and Iain Johnstone
  • Curvelets and Curvilinear Integrals with Emmanuel Cande`s, 1999
  • Recovering Edges in in Ill-Posed Linear Inverse Problems: Optimality of Curvelet Frames with Emmanuel Cande`, 2000
  • Ridgelets: a key to higher-dimensional intermittency? with Emmanuel Cande`s
  • Uncertainty Principles and Ideal Atomic Decomposition with Xiaoming Huo, 1999
  • Smooth Multiwavelet Duals of Alpert Bases by Moment-I
  • Estimating Covariances of Locally Stationary Processes: Convergence of Best-Basis Methods. with Ste'phane Mallat,and Rainer von Sachs, 1998
  • Asymptotic Minimaxity of Wavelet Shrinkage for Sampled Data with Iain Johnstone, 1997
  • Nonlinear `Wavelet Transform' via Median-Interpolation with Thomas Pok-Yin Yu,
  • Renormalizing Experiments for Nonlinear Functionals
  • Deslauriers-Dubuc: Ten Years After with Thomas Pok-Yin Yu, 1996
  • Unconditional Bases and Bit-Level Compression.
  • CART and Best-Ortho-Basis: a connection , 1995
  • Atomic Decomposition by Basis Pursuit with Scott Shaobing Chen and Michael Saunders.
  • WaveLab and Reproducible Research. with Jonathan Buckheit.
  • Translation-Invariant De-Noising. with Ronald R. Coifman.
  • Basis Pursuit with Scott Shaobing Chen
  • Ideal Denoising in an Orthonormal Basis Chosen from a Library of Bases. with Iain Johnstone.
  • On Minimum Entropy Segmentation, 1994(rev)
  • Neo-Classical Minimax Problems, Thresholding and Adaptation with Iain Johnstone.
  • Improved Linear Discrimination with a Time-Frequency Dictionary with Jonathan Buckheit.
  • Smooth Wavelet Decompositions with Blocky Coefficient Kernels
  • Wavelet Shrinkage and W.V.D. -- A Ten-Minute Tour
  • Adapting to Unknown Smoothness via Wavelet Shrinkage. with Iain Johnstone.
  • Density Estimation by Wavelet Thresholding. with Iain Johnstone, Ge'rard Kerkyacharian, and Dominique Picard.
  • Wavelet Shrinkage: Asymptopia. with Iain Johnstone, Ge'rard Kerkyacharian, and Dominique Picard.
  • Nonlinear Wavelet Methods for Recovering Signals, Images, and Densities from indirect and noisy data.
  • Interpolating Wavelet Transforms.
  • Nonlinear Solution of Linear Inverse Problems by Wavelet-Vaguelette Decomposition
  • Unconditional Bases are Optimal Bases for Data Compression and for Statistical Estimation
  • Ideal Spatial Adaptation via Wavelet Shrinkage. with Iain Johnstone.
  • Minimax Estimation via Wavelet Shrinkage with Iain Johnstone.
  • Minimax Risk Over $l_p$-Balls for $l_q$-Error with Iain Johnstone.
  • Statistical Estimation and Optimal Recovery
  • Maximum Entropy and The Nearly Black Obect with Iain Johnstone, Jeffrey Hoch and Alan Stern.
  • Breakdown Properties of Multivariate Location Estimators based on halfspace depths and outlyingness with Miriam Gasko.
  • Geometrizing Rates of Convergence, II with Richard C. Liu.
  • Geometrizing Rates of Convergence, III with Richard C. Liu.
  • Minimax Estimation over Hyperrectangles with R.C. Liu and K.B. MacGibbon
  • Uncertainty Principles and Signal Recovery with P.B. Stark
  • One-sided inference for functionals of a density
  • On Minimum Entropy Deconvolution

E     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z< TopBR>

• Econometrics
  • Ait-Sahalia, Yacine at Economics Department, Princeton
    • Goodness-of-Fit Tests for Regression Using Kernel Methods, with Peter J. Bickel and Thomas M. Stoker, Journal of Econometrics, 2001, 105, 363-412
    • Nonparametric Option Pricing under Shape Restrictions, with Jefferson Duarte, forthcoming in the Journal of Econometrics.
    • The Effects of Random and Discrete Sampling When Estimating Continuous-Time Diffusions, with Per Mykland, Econometrica, 2003, 71, 483-549.
    • Closed-Form Likelihood Expansions for Multivariate Diffusions.
    • Estimating Affine Multifactor Term Structure Models Using Closed-Form Likelihood Expansions
    • Variable Selection for Portfolio Choice, with Michael Brandt, Journal of Finance, 2001, 56, 1297-1351
  • Chernozhukov, Victor
    • Conditional Extremes and Near-Extremes (dissertation)
    • 3-step Censored Quantile Regression, and Extramarital Affairs
    • Conditional Extremes and Near-Extremes
  • Hansen, Bruce
    • Lecture note on Econometrics
  • Hong, Han
    • Empirical likelihood-based model selectin criteria for moment condition models with Preston, B. and Shum, M.
    • Likelihood inference for some non-regular econometric models with Chernozhukov, Victor
    • A semiparametric estimator for dynamic optimatization models, with an application to a milk quota market with Shum, M.
    • Measurment error models with auxiliary data iwth Chen, X. and Tarmer, E.
    • Nonparametric tests for commoon values with Haille, P.
    • Nonparametric tests for common values in first-price sealed-bid auctins with Haille, P.
    • Likelihood ratio tests between parametric and moment condition models with Chen, X. and Shum, M.
    • A fast subsampling method for nonlinear dynamic models with Scaillet, O. and Tarmer, E.
  • Scaillet, Oliver

F     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Jianqing Fan
  • Generalized likelihood ratio statistics and Wilks phenomenon with Chunming Zhang and Jian Zhang
  • eneralized likelihood ratio statistics and Wilks phenomenon with correction
  • Goodness-of-fits tests for parametric regression models
  • Variable selection via nonconcave penalized likelihood and its oracle properties with Runze Li
  • Variable selection for Cox's proportional hazards model and frailty model with Runze Li
  • Efficient estimation of conditional variance functions in stochastic regression with Qiwei Yao
  • Local maximum likelihood and inference with Mark Ferman and Irene Bijbels
  • Simultaneous confidence bands and hypothesis testing in varying-coefficient models with Wenyyang Zhang
  • Test of significance when data are curves
  • Two-step estimation of functional linear models with applications to longitudinal data with Jin-Ting Zhang
  • One-step local quasi-likelihood estimation with Jianwei Chen
  • Generalized partially linear single-index models with R.J.Carroll, Irene Gijbels, and M.P. Wand
  • Prospects of nonparametric modeling
  • Local likelihood and local partial likelihhod in hazard regression with Irene Gijbels and Martin King
  • Varialbe bandwidth and one-step local M-estimator with Jiancheng Jiang
  • Nonparametric estimation of quadratic regression functioonals with LI-Shan Huang
  • Skewing-methods for two-parameter locally-parametric density estimation with Edwin Choi
  • Automatic local smoothing for spectral density estimation with Eva Kreutzberger
  • Local polynomial estimation of regression functions for mixing processes with Elisas Marsry
  • Statistical estimation in varying-coefficient models with Wenyang Zhang
  • Efficient estimation and inference for varying-coefficient models with Zongwu Cai and Runze Li
  • Geometric understanding of LR statistics with Hui-Nien Hung and Wing=Hung Wong
• Finance 関係
  • 関根氏の講義録
  • Stochatic calulus and Finacne by Sherve
• Friedman, J.H.
  • Multivariate adaptive regression splines
  • Tutorial: Getting Started with MART in R
  • Clustering Objects on Subsets of Attributes with Meulman, J. J.
  • On Bagging and Nonlinear Estimation. with Hall , P.
  • Stochastic Gradient Boosting
  • Greedy Function Approximation: A Gradient Boosting Machine
  • Rejoinder to discussion of:: Bump Hunting in High-Dimensional Data with Fisher, N. I.
  • Additive Logistic Regression: a Statistical View of Boosting with Hastie, T. and Tibshirani, R
  • Data Mining and Statistics: What's the Connection?
  • Bump Hunting in High-Dimensional Data. with Fisher, N. I.
  • DART/HYESS Users Guide
  • Another Approach to Polychotomous Classification
  • Local Learning Based on Recursive Covering
  • On Bias, Variance, 0/1-loss, and the Curse-of-Dimensionality
  • Flexible Metric Nearest Neighbor Classification.|
  
  

G     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• van de Geer, Sara
  • Adaptive estimation with soft thresholding penalties with Jean-Michel Loubes
  • M-estimator using penalities or sieves JSPP 108(2002) 55-69
  • On Hoeffding's inequality for dependent random variables.
  • Least squares estimation with complexity penalties.
• 　George, Edward.I.
  • The Practical Implementation of Bayesian Model Selection (with discussion) with Hugh Chipman and Robert McCulloch
  • Bayesian Treed Models with Hugh Chipman and Robert McCulloch
  • Bayesian Model for Collaborative Filtering with Alex Chien
  • Calibration and Empirical Bayes Variable Selection Table
  • The Variable Selection Problem
  • Posterior Propriety in Some Hierarchical Exponential Family Models (with Zuoshun Zhang)
  • Flexible Empirical Bayes Estimation for Wavelets (with Merlise Clyde)
  • Empirical Bayes Estimation in Nonparametric Wavelet Regression (with Merlise Clyde)
  • Maximally Informative Statistics (with David Wolf)
  • Hierarchical Priors for Bayesian CART Shrinkage (with Hugh Chipman and Robert McCulloch)
  • Extracting Representative Tree Models from a Forest (with Hugh Chipman and Robert McCulloch)
  • Bayesian CART Model Search (with Hugh Chipman and Robert McCulloch)
  • Approaches for Bayesian Variable Selection (with Robert McCulloch)
  • A Bayesian Model for Collaborative Filtering (with Alex Chien)
  
  
• Geyer, C.
  • On asympttotics of constrained M-estimation
• Gill, Richard
  • Survey paper On Quantum Statistical Inference (JRSS B, 2003)
  • Tutorial paper Teleportation into Quantum Statistics (JKSS 2001)
  • State estimation for large ensembles (pdf); with S. Massar. Phys. Rev. A 61 (2000), 2312-2327
  • Fisher information in quantum statistics with O.E. Barndorff-Nielsen. J. Phys. A: Math. Gen 30 (2000), 4481-4490.
  • Quantum asymptotics State of the Art in Probability and Statistics; Festschrift for Willem R. van Zwet, IMS monographs 36 (2001), 255-285, ed. A.W. van der Vaart, M. de Gunst, C.A.J. Klaassen.
  • Quantum information with O.E. Barndorff-Nielsen and P.E. Jupp. Mathematics Unlimited - 2001 and beyond (part I), 83-107 (2001), ed. B. Engquist, W. Schmid, Springer.
  • Lecture notes on discrete quantum systems
  • Lecture notes on hidden variables
• Gine-Masdeu, Evarist
  • Unified papers
  • Lecture on some aspects of the bootstrap
  • Lecture on some aspects of the bootstrap ( longer version)
  • Lecture on some aspects of the bootstrap ( shorter version)
  • Correction
  • Laws of the iterated logarithm for censored data with Guillou, Armelle, AP 27(1999), 2042-2067
  • Central limit theorems for the Wasserstein distance between tghe empirical and the true distributions with del Barrio, E. and Matran, C., AP 27(1999), 1009-1071.
  • When is the student t-statistic asymptotically standard normal with Gotze, F. and Mason, D., AP 25(1997), 1514-1531.
  • On the law of iterated logarithm for canonical U-statistics and processes with Arcones, M. Stochastic processes and their application 58(1995), 217-245.
  • Estimators related U-processes with application to multivariate median asymptotic normality with Arcnones, M. and Chen, Zhinqang, AS 22(1994), 1460-1477.
  • Laws of large numbers for quandratic forms, maxima of products and truncated sums of I.I.d. random variables with Cuzick, J. and Zinn, J. AP 24(1995), 292-333.
  • A remark on convergence in distribution of U-statistics with Zinn, J. AP 22(1994), 117-125.
  • Limit theorems for U-processes with Arcones, M., AP 21(1993), 1494-1542
  • On the bootstrap of U and V statitics with Arcnoes, M. 20(1992), 655-674
  • Gaussian characterization of uniform Donsker classes of functions with Zinn, J> AP 19(1991), 758-782
  • Bootstrapping general empirical measures with Zinn, J. AP 18(1990), 851-869
  • Some bootstap tests of symmetry for univariate continuous distributions with Arcones, M. AS 19(1991), 1496-1511
  • Necessary condtions for the bootstrap of the mean with Zinn, J. AS 17(1989), 684-691
  • The law of large numbers for partial sum processes indexed by sets with Zinn, J. AP 15(1987), 154-163
  • Empirical processes indexed by Lipschitz functions with Zinn, J. AP 14(1986), 1329-1338
  • Some limit theorems for empirical processes with Zinn, J. AP 12(1984), 929-989 with discussion by
  • Rates of strong uniform consistency for multivariate kernel density estimators with Guillou, A.
  • Exponential and moment inequalities for U-statistics with Latala, R. and Zinn, J.
  • The L_1 norm density estimator processs with Mason, D.M., Zaitsev, A.Y.
  • The LIL for canonical U-statisticsof order 2 with Kwapien, S. and Latala, R. and Zinn. J.
  • Kernel density estimators: convergence in distribution for weighted sup norms
  • Asymptotics for L2 functionals of the quantile process with applications to tests of fit based on Weighted Wasserstein distances
• Groeneboom, Piet
  • Caput course Selected Topics from Medical Statistics and Theory of Stochastic Processes, Fall 2001
  • EM algorithm
  • 593C
  • Kernel estimators for the extreme value index with H. P. Lopuhaa, and P. P. de Wolf, to appear in AS
  • Asymptotically optimal estimation of smooth functionals for interval censoring, case 2 with Ronald Geskus, AS April 1999
  • Integrated Brownian motion, conditioned to be positive with Geurt Jongbloed and Jon A. Wellner, Annals of Probability, July 1999
  • A monotonicity property of the power function of multivariate tests with Donald R. Truax, Indagationes Mathematicae, June 2000.
  • A canonical process for estimation of convex functions: the ``invelope'' of integrated Brownian motion +t^4 with Geurt Jongbloed and Jon A. Wellner. Annals of Statistics. December 2001.
  • Asymptotic normality of the L1-error of the Grenander estimator with Hendrik P. Lopuhaa and Gerard Hooghiemstra, Annals of Statistics, August 1999
  • Estimation of a Convex Function: Characterizations and Asymptotic Theory with Geurt Jongbloed and Jon A. Wellner. Annals of Statistics. December 2001.
  • Nonparametric estimation of the lifetime and disease onset distributions for a survival-sacrifice model with Antonio Eduardo Gomes and Jon.A. Wellner, August 2001. Submitted to Annals of Statistics.
  • Density estimation in the uniform deconvolution model with Geurt Jongbloed. Submitted for publication (2002).
• Gu Ming-gao
  • On the Bartlett adjustment for the partial likelihhhod ratio test in the Cox regression model
  • Monitoring a general class of two-sample survival statistics with application
  • Edgeworth expansion and Cox model
  • A stochastic approximation algorithm for MLE with incomplete data

H     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Haberman(Nowternwest University)
  • Lecture note on Survival Analysis
  • Survival Analysis で合成できなかった部分
  • Lecture note on GLM
  • Lecture note on Linear Regeression
  • Lecture note on Linear Regeressionで合成できなかった分布
• Handbook of Econmetrics Vol. 1--5
  • Volumun 1
    • Linear algebra and matrix methods in Econometrics by Theil, H.
    • Statistical theory and Econometrics by Zellner, A.
    • Economic and Econometric models by Intriligator, M.
    • Identification by Hsiao, C.
    • Model choice and specification analysis by Leamer, E.E.
  • Volumun 2
  • Volumun 3
  • Volumun 4
    • Large sample estimation and hypothesis testing by Whitney K. Newey and Daniel McFadden
    • Empirical Process Methods in Econometrics by Donald W. K. Andrews
    • Applied Nonparametric Methods by Wolfgang H?rdle and Oliver Linton
    • Bootstrap by Hall, P.
    • Classical Estimation Methods for LDV Models Using Simulation Vassilis A. Hajivassiliou and Paul A. Ruud
    • Estimation of Semiparametric Models by James L. Powell
    • Restrictions of Economic Theory in Nonparametric Methods　by Matzkin R.L.
    • Restrictions of Economic Theory in Nonparametric Methods by Manski, C.F.
    • Restrictions of Economic Theory in Nonparametric Methods by C. Gourieroux and A. Monfort
    • Estimation and Inference for Dependent Processes by Jeffrey M. Wooldridge
  • Volumun 5
• 　Hansen at Bell
  • Unified papers with Bin Yu
  • thesis
  • Wald lecture
  • Spline adaptation in extended linear models
  • Minimum description lenght model selection criteria for generalized linear model
  • Model selection and the principle of MDL
  • Wavelet thersholding via MDL for natural images
• Hastie, Trevor
  • Unified papers unpublished
  • Unified papers
  • Independent Component Analysis through Product Density Estimation with Robert Tibshirani
  • Boosting as a Regularized Path to a Maximum Margin Classifier with * Saharon Rosset, Ji Zhu
  • Support Vector Machines, Kernel Logistic Regression and Boosting Slides for talk given at Spring research conference in Michigan, MCS2002 in Sardinia, NPCONF2002 in Crete, and ASA2002 in New York.
  • Class prediction by nearest shrunken centroids, with applications to DNA microarrays with * Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu
  • Least Angle Regression with Bradley Efron, Iain Johnstone and Robert Tibshirani,
  • Feature extraction for non-parametric discriminant analysis with Mu Zhu
  • Kernel Logistic Regression and the Import Vector Machine with Ji Zhu
  • Exploratory screening of genes and clusters from microarray experiments with * Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, Michael Eisen, Gavin Sherlock, Pat Brown, and David Botstein
  • Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 98: 10869-10874. with * Therese Sorlie, Perou, C., Robert Tibshirani, Turid Aas, Stephanie Geisler, Hilde Johnsen, Trevor Hastie, Michael B. Eisen, Matt van de Rijn, Stefanie S. Jeffrey, Thor Thorsen, Hanne Quist, John C. Matese, Patrick O. Brown, David Botstein, Per Eystein Lonninngg, and Anne-Lise Borresen-Dale.
  • Supervised Harvesting of Expression Trees with Robert Tibshirani, David Botstein and Pat Brown,
  • Missing value estimation methods for DNA microarrays with Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, BIOINFORMATICS Vol. 17 no. 6, 2001 Pages 520-525
  • Degrees-of-Freedom Tests for Smoothing Splines. with * Eva Cantoni
  • Reduced Rank Vector Generalized Linear Models with Thomas Yee
  • Estimating the number of clusters in a dataset via the Gap statistic with * Robert Tibshirani, Guenther Walther
  • Flexible Statistical Models for Growth Fragments: a Study of Bone Mineral Acquisition with Laura Bachrach, Balasubramanian Narasimhan and May Choo Wang
  • Functional Linear Discriminant Analysis for Irregularly Sampled Curves with * Gareth James, Journal of the Royal Statistical Society, Series B
  • Gene Shaving: a New Class of Clustering Methods for Expression Arrays with Robert Tibshirani, Michael Eisen, Pat Brown, Doug Ross, Uwe Scherf, John Weinstein, Ash Alizadeh, Louis Staudt, David Botstein
  • Imputing Missing Data for Gene Expression Arrays with Tibshirani, R., Sherlock, G., Eisen, M., Brown, P. and Botstein, D.
  • Clustering methods for the analysis of DNA microarray data with Tibshirani, R., Eisen, M., Ross, D. , Botstein, D. and Brown, P.
  • A Principal Component Models for Sparse Functional Data with James, G.
  • Optimal kernel shapes for local linear regression with * D. Ormoneit
  • The Global Pairwise Approach to Radiation Hydrid Mapping with * Tibshirani, R. and Lazzeroni, L. and Hastie, T. and Olshen, A. and Cox, D.R.
  • Additive Logistic Regression: a Statistical View of Boosting with Friedman, J., and Tibshirani, R.
  • Statistical Models for Image Sequences with * Crellin, N., and Johnstone, I
  • Bayesian Backfitting with Tibshirani, R.
  • Regression Analysis of Multiple Protein Structures with T., Schmidler, S. and Brutlag, D.
  • Discriminative vs Informative Learning with Rubenstein, D.
  • Dynamic Mixtures of Splines: a Model for Saliency Grouping in the Time Frequency Plane with Maes, S.
  • Flexible Discriminant and Mixture Models with Tibshirani, R. and Buja, A.
  • Modelling and superposition of multiple protein structures using affine transformations: analysis of the globins with * Wu, T., Schmidler, S., and Brutlag, D.
  • Generalizations of the Bias/Variance Decomposition for Prediction Error with James, G.
  • Neural Networks
  • Classification by Pairwise Coupling with Tibshirani, R.
  • Generalized Additive Models with Tibshirani, R.
  • Models and Metrics for Handwritten Character Recognition with Simard, P.
  • Discriminant Adaptive Nearest Neighbor Classification. with Tibshirani, R., IEEE PAMI, 18, 607-616, 1996.
  • Learning Prototype Models for Tangent Distance. with Simard, P. Y., and Saeckinger, E.
  • Handwritten Digit Recognition via Deformable Prototypes. with Tibshirani, R.
  • Flexible Discriminant Analysis by Optimal Scoring. with Tibshirani, R. and Buja, A., JASA, December 1994.
  • Automatic Smoothing Spline Projection Pursuit with Roosen, C. B.
  • Logistic Response Projection Pursuit with Roosen, C. B.
  • A Model for Signature Verification with Kishon, E., Clark, M., and Fan, J.
  • Shrinking Trees. with Pregibon, D.
• N. E. Heckman
  • Testing for monotonicity of a regression mean without selecting a bandwidth with Hall, P.
  • Computing the shape of regression functions with Zamar, R.
  • CriSP-a tool for bump hunting with Harezlak, J.
  • Maclean's ranking of Canadian Universityies with White, R., Wang, S., and Wang H.
  • Nonparametric testing for a monotone hazard function via normalized spacings withGijbels, I.
  • Log harzard regression by Huiying Sun
  • Local linear forecasting with Li, X.
  • Estimating and depicting the sturcture of a distribution of random function with Hall, P.
  • The theory and application of penalized least sqares methods or reproducing KHS made easy
  • Penalized regression with model-based penalities with Ramsay, J.O.
  • Comparing the shapes of regressin functions with Zamar, R.
  • Line transects of two dimensional random fields: estimation and design with Rice, J.
• Horowitz, J. at Department of Economics, Nothernwestern University
  • The bootstrap
  • Nonparametric estimation of an addtive model with a link function
  • An adaptive, rate-optimal test of linearity for median regression models
  • Bootstrap critical values for tests based on the smoothed maximum score estimator
  • Bootstrap methods for time series
  • Bootstrap methods for Markov processes
  • Bootstrapping the Box-Pierce Q test
• 　Huang Jianhua
  • Statistical modeling of diffusion processes with free knot splines with Stone, C.(2002). Journal of Statistical Planning and Inference To appear.
  • Extended linear modeling with splines with Stone, C. J. (2002). In Nonlinear Estimation and Classification. Lecture Notes in Statistics. Springer, New York.
  • Free knot splines in concave extended linear modeling with Stone, C.(2002)
  • Projection estimation in multiple regression with application to functional ANOVA models (1998)The Annals of Statistics Vol 26, 242-272
  • Functional ANOVA models for generalized regression (1998)The Journal of Multivariate Analysis Vol. 67, 49-71
  • Varying-coefficient models and basis function approximations for the analysis of repeated measurements with Wu, C. O. and Zhou, L. (2002) Biometrika Vol 89, 111-128.
• Hunter, David
  • Lecture note on Bioinformatics
  • Lecture note (Beatnix
  • Lecture note on Asymptotic tool

I     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Irizarry, Rafael A.
  • An investigation of the second- and higher- order spectra of music. Signal Processing. 65: 161--178
  • Information and Posterior Probability Criteria for Model Selection in Local Likelihood Estimation . Journal of the American Statistical Association. 96: 303-315
  • Local Regression with Meaningful Parameters. American Statistician. 55: 72-79
  • Local Harmonic Estimation in Musical Sound Signals. Journal of the American Statistical Association|
  • Assessing Homeostasis through Circadian Patterns. Biometrics. 57: 1228-1237
  • . A Statistical Analysis of Radiolabeled Gene Expression Data Proceedings of Interface 2001
  • Weighted estimation of harmonic components in a musical sound signal. Journal of Time Series Analysis. 23: 29-48.
  • Generalized Additive Selection Models for the Analysis of Non-ignorable Missing Data. Submited to Biometrics
  • Choosing the smoothness parameter for smoothing splines by minimizing an estimate of risk
  • A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance To appear in Bioinformatics
  • xploration, Normalization, and Summaries of High Density Oligonucleotide Array Probe Level Data. Accepted for publication in Biostatistics
  
  

J     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Iain Johnstone
  • Lecture note
• Michael Jordan
  • Lecture note on Combinatrics and Probability
  • Kernel Independent Component Analysis
  • Machine Learning Lecture notes
  • Saul's Lecture notes
  • Manrina's Thesis
  • An introduction to Variateion methods to graphical models
  • Hierarchicak mixtures of experts and the EM algorithm
  • Graphical models: probabilistics inference
  • Kernel independent components analysis
  • An introduction to MCMC for machine learning manusript
  • An introduction to MCMC for machine learningwhich is taken from Machine Learning
  
  

K     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Wilbert C.M. Kallenberg
  • Vanishing shortcoming and asymptotic relative efficiency with Inglot, T. and Ledwina, T.
  • Mpderate derivations of MCEs under contamination with Inglot, T.
  • Data driven rank tests for classes of tail alternatives with Alberts, W., Martini, F.
  • Size and power of pro-test procedures with Alberts W., Boon, P.C.
  • Moderate derviations of MLE under alternatives
• Keilengom, I.V.
  • Estimation of semiparametric models when the crieterion function is not smooth
  • Bootstrap confidence bands for regression curves and their derivatives
  • Nonparametric analysis of covariance for censored data
  • Estimation of bivariate marginal distributions with censored data
  • Density and harzard estimation in censored regression models
  • Nonparametric estimation of residual distribution
  • Inference on multivariate M-estimators based on bivariate censored data
  • Estimation of the conditional distribtuion in regression with censored data
  • The least squares method in heteroseedastic censored regression models
  • Transofer of tail informatin in censored regression models
• Koenker, Roger
  • Lecture note on Quantile regression
  • A brief tutorial on quantile regression
  • Goodness of Fit and Related Inference Processes for Quantile Regression with Jose A.F. Machado. The paper appears in J. of Am Stat. Assoc, (1999), 94, 1296-1310
  • Short course on quantile regression
• Kim, Yongdai at Ewha Womans University, Department of Statistics
• Knight
  • 講義録
  • 講義録 (Econometrics から)
  • 講義録
  • What are the limiting distributin of equantile estimators
  • A brief introduction to S-plus and maple
  • Epi-convergence in distribution and stochastic equi-semicontinuity
  • Asymptotics for L_1 regression estimators under general conditions
  • Limiting distributions of linear programming estimators
  • Comparing conditional quantile estimators: first and second order considerations
  • On the second order behaviour of the bootstrap of L_1 regression
  • Convergence of argmin estimators OHP
  • Asymptotic of regression shrinkage /selectin estimation OHP
  • Bahaur-Kiefer theorems and the bootstrap OHP
• Michael Kohler
  • Nichtparametrische Regressionsscha tzung mit Splines. PhD thesis, University of Stuttgart, Germany, 1997.
  • A distribution free theory of nonparametric regression, chaper 1
  • Nonparametric estimation of piecewise smooth regression functions. Statistics and Probability Letters 43, pp. 49-55, 1999.
  • On optimal global rates of convergence for nonparametric regression with random design. Submitted for publication.
  • Nonasymptotic bounds on the $L_2$ error of neural network regression estimates, Submitted for publication. (joint work with M. Hamers).
  • A simple proof of approximation properties of neural networks, Submitted for publication. (joint work with A. Krzyzak).
  • Bound on the expected maximal deviation of averages from their means, Submitted for publication. (joint work with M. Hamers).
  • Strong consistency of automatic kernel regression estimates, Submitted for publication. with A. Krzyzak and H. Walk
  • How well can a regression function be estimated if the distribution of the (random) design is concentrated on a finite set? Submitted for publication. (joint work with M. Hamers).
  • Prediction from randomly right censored data , Journal of Multivariate Analysis 80, pp. 73-100, 2002. (with K. Ma'the' and M. Pinte'r).
  • Nonlinear orthogonal series estimates for random design regression. To appear in Journal of Statistical Planning and Inference, 2002.
  • Inequalities for uniform deviations of averages from expectations with applications to nonparametric regression. Journal of Statistical Planning and Inference 89, 1-23, 2000.
  • Lower bounds on the rate of convergence of nonparametric regression estimates, Journal of Statistical Planning and Inference 83, pp. 91-100, 2000. (with A. Antos and L. Gyorfi).
  • Universally Consistent Regression Function Estimation Using Hierarchical B-Splines. Journal of Multivariate Analysis 67, pp. 138-164 , 1999.
  • Prediction from randomly right censored data, Journal of Multivariate Analysis 80, pp. 73-100, 2002. (with K. Ma'the' and M. Pinte'r).
• Kolaczyk, Eric D. Assistant Professor Department of Mathematics and Statistics Boston University
  • Thesis
  • Gaussian mixture discriminant analysis and sub-pixel land cover classification in remote sensing. Remote Sensing of Environment, (in press). with Gopal, S. (2002).
  • A multiresolution analysis for likelihoods: theory and methods. with Nowak, R.D. (2000).
  • Multiscale likelihood analysis and complexity penalized estimation. Annals of Statistics, (to appear). with Nowak, R.D. (2003).
  • Multiscale generalized linear models for nonparametric function estimation. (Submitted) with Nowak, R.D. (2003).
  • Risk analysis for multiscale penalized MLEs
• Kooperberg
  • Unified papers
  • Spline adaptation in extended linear models with Hansen, M.H.
  • Confidence intervals for logspline density estimation with Stone, C.J.
  • Comparision of paarmetric, Bootstrap, and Bayesian approches to obtaining confidence intervals for logspline density estimation with Stone, C.J.
  • Hazard regression with Stone, C.J. and Truong, Y.K.
  • Hazard regression with interval-censored data with Charlson, D.B.
  • The L_2 rate of convergence for Hazard regression with Stone, C.J. and Truong, Y.K.
  • Rate of convergence for logspline spectral density estimation with Stone, C.J.
  • Logspline density estimation under censoring and truncation with Koo, Ja-Yong, and Park, Jinho.
  • Logspline density estimation for censored data with Stone, C.J.
  • Functional ANOVA modeling for proportional hazards regression with Huang, Jinhua, Z., Stone, C.J. and Truong, Y.K>
  • Polynomial splines and their tensor products in extended linear modeling with Stone, C.J.
• Koul, Hira
  • Asymptotics of mle in a two-phase linear model with Qian, Lianfen, JSPI 108(2002) 99-109.
  • Asymptotics of M-estimators in two-phase linear regresssion models with Qian, L. and Surgailis, Donatas, Stochastic processes adn their application 103(2003) 123-154.
• Adam Krzyzak
  • some paper

L     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• van der Laan
  • Unified papers on Genetics
  • Unified papers on efficient estimation
  • Estimation of the bibariate survival function with generalized bivariate right censored data structures
  • Inefficinet estimators of the bivariate survival functin for three models
  • Efficient estimation in the bivariate censoring model and repairing NPMLE
  • Locally efficient estimatin in censored data models
  • Nonparametric locally efficient estimation fo the treatment specific survival distribution with right censored data
  • Efficiency of MPMLE in nonparametric missing data models
  • The NPMLE in a class of doubly censored current status data models
  • Current status and right-censored data stuctures when oabserving a marker at the censoring time
  • Inference with bivariate trncated data
  • Identity for the NPMLE in censored data models
  • Efficient estimation from right-censored data when failure indicators are missing at random
  • Identity for NPMEL in missing and based sampling models
  • Locally efficient estimation of the survivial distribution with right censored data and covariates when collection of data is delayed
  • Coarsening at random
  • Nonparametric estimation of the bivariate survival function with truncated data
  • Locally efficient estimation of a multivariate survival function in longitudinal studies
  • The NPMLE for doubly censored current status data
  • Locally efficient estimation of the quality adusted lifetime distribution with right-censored data
  • Unified papers
• Least absolute deviation
  • Estimation of mutilpe-regime regressions with least absolutes deviation by Jushan Bai, JSPI 74(1998), 103-134
  • A general Akaike-type criterion for model selection in robust regression by Burman, P. and Nolan, D. Biometrika 82(1995), 877-886
  • Prediction via estimating functions by Thavansswaran, A. and Heyde, C.C., JSPI 77(1999), 89-101
  • The L_1 method for robust nonparametric regression by Wang, F.T. and Scott, D.W. JASA, 89(1994), 65-76
  • Linear programming techniques for regression analysis by Wanger, H.M., JASA
• 　Li, Bing
  • A minimax approach
  • Potential functions
  • A deviance function
  • Projected partial likelihood
  • Sensivity of tests to nuisance parameters
• Lin, Ying at University of Wisonsin
  • Some Asymptotic Properties of the Support Vector Machine
• Linton, Oliver
  • ESTIMATING SEMIPARAMETRIC ARCH(?) MODELS BY KERNEL SMOOTHING METHODSwith Enno Mammen
  • Estimation of Semiparametric Models when the Criterion Function is not Smooth with Xiaohong Chen and Ingrid Van Keilegom
  • Semiparametric Regression Analysis under Imputation for Missing Response Data with Qihua Wang, Wolfgang Hardle
  • MORE EFFICIENT KERNEL ESTIMATION IN NONPARAMETRIC REGRESSION WITH AUTOCORRELATED ERRORS with Zhijie Xiao, Raymond J. Carroll, and E. Mammen
  • Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems with S. Berry and A. Pakes
  • Consistent Testing For Stochastic Dominance: A Subsampling Approach with Esfandiar Maasoumi and Yoon-Jae Whang
  • ESTIMATING FEATURES OF A DISTRIBUTION FROM BINOMIAL DATA with Arthur Lewbel and Daniel McFadden
  • The Common and Specific Components of Dynamic Volatility with G.C.Connor and R.A. Korajczyk
  • Asymptotic Expansions for some Semiparametric Program Evaluation Estimators with H. Ichimura
  • An Alternative way of Computing Efficient Instrumental Variable Estimators with M. Shintani
  • Is there chaos in the world economy? A nonparametric test using consistent standard errors with M. Shintani
  • A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form
  • Nonparametric Estimation of a Multifactor Heath-Jarrow-Morton model: An Integrated Approach with Andrew Jeffrey, Thong Nguyen, and Peter C.B. Phillips
  • Accounting for Correlation in Marginal Longitudinal Nonparametric Regression with Enno Mammen, Xihong Lin, and Raymond Carroll
  • Estimation of Linear Regression Models from Bid-Ask Data by a Spread-Tolerant Estimator
  • Flexible Term Structure Estimation: Which Method is Preferred? with Thong Nguyen and Andrew Jeffrey)
  • Second order approximations for adaptive regression estimators with Z. Xiao
  • Edgeworth approximation for semiparametric instrumental variable estimators and test statistics
  • Some higher order theory for a consistent nonparametric model specification test with Y. Fan
  • Second order approximation in the partially linear regression model
  • The existence and asymptotic properties of a backfitting algorithm under weak conditions with E. Mammen and J. Nielsen
  • Estimating Additive Nonparametric Models by Partial L_q Medianning: The Curse of Fractionality
  • Estimating Multiplicative and Additive Marker Dependent Hazard Functions by Backfitting with the Assistance of Marginal Integration
  • Estimating Multiplicative and Additive Marker Dependent Hazard Functions by Kernel Methods
  • Testing Additivity in Generalized Nonparametric Regression Models with Estimated Parameters with P. Gozalo
  • An Analysis of Transformations for Additive Nonparametric Regression with R. Chen, N. Wang, and W. Hardle.
  • Efficient estimation of generalized additive nonparametric regression models
  • The Shape of the Risk Premium: Evidence from a Semiparametric GARCH Model with B. Perron
  • Semiparametric Estimation of a Characteristic-based Factor Model of Stock Returns with G. Connor
  • Yield Curve Estimation by Kernel Smoothing with E. Mammen, J. Nielsen, and C. Tanggaard
  • The Asymptotic Distribution of Nonparametric Estimates of the Lyapunov Exponent for Stochastic Time Series with Y. Whang
  • Testing the Capital Asset Pricing Model Efficiently under elliptical symmetry: A semiparametric approach with D. Hodgson and K. Vorkink
  • A GARCH Model of the Implied Volatility of the Swiss Market Index From Option Prices with M. Sabbatini
  • Nonparametric Censored and Truncated Regression with A. Lewbel
  • Local Nonlinear Least Squares: Using Parametric Information in Nonparametric Regression with P. Gozalo
  • Testing Conditional Independence Restrictions with P. Gozalo
  • Nonparametric Estimation with Aggregated Data with Y. Whang
  • A Nonparametric Prewhitened Covariance Estimator with Z. Xiao
  • Nonparametric Factor Analysis of Residual Time Series (with J.M. Rodriguez-Poo
  • Lecture note on nonlinear methods for Econometrics
  • Some backgroudn knowldge
  • Lecture note
  • Lecture note on Probability and Statistics
  • Lecture note on methods of Economic investiagation II
  • Topic on finacial markets
    • Lecture 2
    • Lecture 3
    • Tyurin Paper
    • Lecture 4
    • Lecture 5
  • and A. Jeffrey
• Lugosi
  • Moment inequalities for functions of independent random variables. with S. Boucheron, O. Boucheron, and P. Massart
  • Complexity regularization via localized random penalties. with Wagkamp
  • Concentration-of-measure inequalities presented at the Machine Learning Summer School 2003, Australian National University, Canberra.
  • Concentration inequalities using the entropy method with S. Boucheron, G. and P. Massart, in AP
  • On the Bayes-risk consistency of regularized boosting methods. with N. Vayatis, in AS
  • Potential-based algorithms in on-line prediction and game theory. in Machine learning
  • Model selection and error estimation. with P. Bartlett and S. Boucheron in Machine learning
  • Lecture note
  • Lecture note
  • Lecture note
  • Lecture note on Concentration-of-measure inequalities presented at the Machine Learning Summer School 2003, Australian National University, Canberra.
  • Concentration Inequality
  • Boosting methodsw
  • Complexity regularization with Wagkamp
  • Data-based margin methods
  • Sharp concentration inequalities
  • Model selections
  • Kernels
  • Goodness of fit
  • Pattern classification and learning theory

M     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• 　MacCullagh
  • Unified Papers
  • Statistical modelA.S.
  • Quasi-symmetry and representation theory, July 2001.
  • Re-sampling and exchanbable arrays, Nov. 1997.
  • Invariance and fractorial models, Dec. 1998.
  • A theory of statistical models for Monte-Carlo integration with Kong, A., Meng, X.L., Nicolion, D., andn Tan, Z., July 2002.
  • The algebratic structure of GLM, May 1999.
  • The proporsional-odd model
  • Model formulae for homologous factors, Feb. 1998
  • Quotient spaces and statistical models, Sep. 1998.
  • Marginal likelihood for Gaussian models
  • Category representations and factorial models, Jun. 1998.
• Machine Learning
  • Learning theory
    • Machine Learning: Foundations Yishay Monsour at http://www.math.tau.ac.il/~mansour/
    • Papers
    • Probabilistic analysis of learning in artifical neural networks: the PAC model and it variants by Martin anthony
    • Kernel methods Champell
    • Weighted least square SVM Suykens
  • Statistical learning theory at MIT
    • On mathematica foundations of learning by Chucker and smale
    • Regularization networks and SVM
    • Equivalence between spare approximation and SVM
    • Stability and generalizatoin
    • Regularization theory and neural networks architectures
    • On the relationship between genaralizaton error, hypothessisc complexity by Girosi
    • Choosing multiple parameters for SVM
    • A unified approach for for regularizatoin networks and SVM
    • SVM for multivariate density estimation
  • SVM
    • Tutorial　こちらはうまく編集してある
    • Tutorial
    • Support Vector Learning by Scholkopf
    • Probability Inference using MCMC
    • Unified review of linear Gaussian models
    • Regularization principl manifolds by Smola
  • 　情報論的学習理論関係
    • Akaho's Unified papers
    • Akaho's Unified papers in Japanese
    • Akaho's thesis
    • Ikeda's thesis
    • Ikeda's paper
    • papers on model selection
• MaPhySto Lecture Notes
  • An introduction to p-variation and Young integration by R.M. Dudley
  • Stochastic Simulation by S. Asmussen
  • Marked Point Processes and Piecewise Deterministic Process by M. Jacobsen
  • Notes on Empirical Processed by R.M. Dudley
  • Empirical and partial-sum prcesees by P. Gaenssler
  • Density Transformation in Levy Processes by K. Sato
• Muller, Hans-Georg
  • Generalized Functional Linear Models
  • Semiparametric Modeling of Labeled Cell Kinetics
  • Functional Canonical Analysis
  • Functional Convex Averaging (Warping)
  • Order-Preserving Nonparametric Regression
  • Functional Quasi-Likelihood Regression Models
  • Functional Multiplicative Effects Model for Longitudinal Data
• 　Murata
  • An approach to blind source separation based on temporal structure of speech signals with Shiro Ikeda and Andreas Ziehe, Neurocomputing, 41:1-24, 2001.
  • Statistical analysis of learning dynamics with Shun-ichi Amari, Signal Processing, 74(1):3-28, 1999.
  • Statistical Study on On-line Learning, In David Saad editor, On-line Learning in Neural Networks, Cambridge University Press, December 1998.
  • Asymptotic Statistical Theory of Overtraining and Cross-Validation with * Shun-ichi Amari, Noboru Murata, Klaus-Robert Mueller, Michael Finke, and Howard Hua Yang, IEEE Trans. Neural Networks, 8(5):985-996, September 1997.
  • A Method of Blind Separation on Temporal Structre of Signals wiht Shiro Ikeda, Proceedings of the fifth International Conference on Neural Information Processing, Vol. 2, pp. 737-742, Japanese Neural Network Society, Kitakyushu, October, 1998.
  • An On-line Algorightm for Blind Source Separation on Speech Signals with Shiro Ikeda, Proceedings of 1998 International Symposium on Nonlinear Theory and its Applications, Vol. 3, pp. 923-926, Research Society of Nonlinear Theory and its Applications, IEICE, September, 1998.
  • An Approach to Blind Source Separation of Speech Signals with Shiro Ikeda, Proceedings of the 8th International Conference on Artificial Neural Networks, Vol. 2, pp. 761-766, Skovde, Sweden, September, 1998.
  • Statistical Analysis of Regularization Constant - From Bayes, MDL and NIC Points of View with Shun-ichi Amari, International Work-Conference on Artificial and Natural Neural Networks 97.
  • Network information criterion - determining the number of hidden units for an artificial neural network model with Shuji Yoshizawa, and Shun-ichi Amari, Technical Report METR 92-05, University of Tokyo, Tokyo, Japan, June 1992.
  • Function approximation by three-layered networks and its error bounds - an integral representation theorem, Technical Reports METR 94-19, University of Tokyo, Tokyo, Japan, October 1994.
  • An Approach to Blind Source Separation Based on Temporal Structure of Speech Signals with Shiro Ikeda and Andreas Ziehe, BSIS Technical Reports No.98-2
  • Independent Component Analysis in the presence of Gaussian Noise with Motoaki Kawanabe, METR 2000-0w (University of Tokyo).
  • 独立成分分析 大阪大学人間科学研究科 集中講義 (2002年9月2日-6日(予定))
  • Stochastic approximations and efficient learning with Botton, Leon.
  • 確率・統計講義ノート
• Murphy, Susan
  • Unified Papers
  • Marginal Mean Models for Dynamic Regimes with M.J. van der Laan, J. Robins, JASA, 96, 1410-1423.
  • Two Level Proportional Hazards Models, Biometrics, 58(4), 180-188.
  • On Profile Likelihood with Van der Vaart, JASA (with discussion), 95, 449-485.
  • Observed Information in Semiparametric Models with Van der Vaart, Bernoulli, 5, 381-412.
  • Semiparametric Mixtures in Case-Control Studies, with Van der Vaart, JMA
  • Semiparametric Likelihood Ratio Inference with Van der Vaart, Annals of Statistics , 25, 1471-1509.
  • MLE in the Proportional Odds Model with A.J. Rossini and A.W. van der Vaart, JASA. , 92, 968-976.
  • Ignorable Dropout in Longitudinal Studies wiht * Tipa, E.M., S.A. Murphy and D.K. McLaughlin (1997)
  • Likelihood Inference in the Errors-in-Variables Model with Van der Vaart, J. of Multivariate Analysis , 59, 81-108
  • A Central Limit Theorem for Local Martingales with Applications to the Analysis of Longitudinal Data, Scand. J. of Stat., 22 279-294.
  • Likelihood Ratio-Based Confidence Intervals in Survival Analysis, JASA ,90 1299-1405.
  • Projected Partial Likelihood and its Application to Longitudinal Data with B. Li, Biometrika,82 399-406.
  • Asymptotic Theory for the Frailty Model, Annals of Statistics ,23 182-198.
  • Consistency in a Proportional Hazards Model Incorporating a Random Effect, Annals of Statistics, 22 712-731.

N     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• National University of Singapole
  • Ali, R. Ayesha
    • Applying Graphical Models to Partially Observed Data Generating Processes(Thesis).
    • Markov Equivalence Classes for Maximal Ancestral Graphs with Thomas S. Richardson (UAI 2002 Conference Proceeding).
  • Bai Zhidong
    • Model selection with data-oriented penalty. with Rao, C. R. and Wu, Y. (1999)
    • A paradox in least squares estimation of linear regression models., with Guo, M. H, JSPI(1999)
    • Asymptotic properties of adaptive designs for clinical trials with delayed response with Rosenberger, W.F., AS(2002)
    • Asymptotic distributins of the maximal depth estimators for regression and multivariate location with He, Xuming, AS(1999)
  • Louis H. Y. CHEN
    • Normal approximation under local dependence With Qi-Man Shao)
    • Asymptotic normality of scaling functions With T. N. T. Goodman and S. L. Lee
    • Stein's method, Palm theory and Poisson process approximation With Aihua Xia
    • Nonrandom Clusters of Palindromes in Herpesvirus Genomes With K. P Choi, Ming-Ying Leung and Aihua Xia)
  • Chen Song Xi
    • Confidence interval based on a local linear smoother. Scand. J. Statist., 2002, 29, 89-99 with Qin, Yong Song
    • Local linear smoothers using asymmetric kernels. Annals of the Institute of Statistical Mathematics, 2002, 54,312-323.
    • Sequential line transect surveys Biometrics, 2002, with Yip, P and Zhou, Y.
    • . Empirical likelihood based confidence intervals for data with possible zero observations. with Qin, J.
    • Simultaneous Specification Tests for the Mean and Variance Structures of Regression with Applications to Testing of Diffusion Models. with Lin, M.
    • The Use of Uncategorized Data to Improve Estimation Efficiency in Mixture Models via Empirical likelihood. with Qin, J.
    • An empirical likelihood goodness-of-fit test for time series. with Haredle, W. and Li, M
    • Effects of bagging and bias correction on estimators defined by estimating functions Figure 1 (to appear in Statistica Sinica) with Hall, P.
  • Chen Zehua
    • Ranked Set Sampling --- Theory and Applications. Springer-Verlag, with Z. D. Bai and B. K. Sinha
    • Asymptotic properties of the remedian
    • On expected lengths of predictive intervals
    • An EM-procedure for the estimation of lifetime distribution using degradation data
  • TRUONG YOUNG K
    • Asymptotics for hazard regression
  • Jin-Ting Zhang
    • SiZer for smoothing splines with Marron, J.S.
    • Local polynomial mixed-effecte models for longitudinal data with Hulin Wu.
• Newey
  • Empirical likelihood estimation and consistent tests with conditional moments restrictions
  • GMM efficient bootstrapping and improved inference
  • Higher order properties of GMM and generalized ELE's
  • Jacknife and analytial bias reduction for nonlinear panel models
  • Asymptotic bias and equvalence of GMM and GEL estimators
  • Nonparametric estimation of sample selection models
• 　Nussbaum M.
  • Asymptotic equivalence of density estimation and Gaussian white noise. Ann. Statist. 24, 2399-2430 (1996 ).
  • Asymptotic equivalence of density estimation and Gaussian white noise. Preprint, longer version.
  • symptotic equivalence of density estimation and Gaussian white noise( preprint, more sections)
  • On the estimation of a support curve of indeterminate sharpness. J. Multivariate Analysis, 62 (2), 204-232 (1997). with Hall, P., and Stern, S. E
  • M: Asymptotic equivalence for nonparametric generalized linear models. Prob. Theor. Rel. Fields, 111 (1998), 167-214 with Grama, I.
  • Diffusion approximation for nonparametric autoregression. Prob. Theor. Rel. Fields 112 (1998), 535-543. with Milstein, G.
  • The asymptotic minimax constant for sup-norm loss in nonparametric density estimation. Bernoulli 5 (1999) 1099-1118 with Korostelev, A.
  • Minimax risk: Pinsker bound. In: Encyclopedia of Statistical Sciences, Update Volume 3, 451-460 (S. Kotz, Ed.) 1999. Wiley, New York. Survey paper written as an Encyclopedia entry.
  • Maximum likelihood estimation of a nonparametric signal in white noise by optimal control. Statistics and Probability Letters, 55 (2) 193-203 (2001) with Milstein. G.
  • M., Asymptotic equivalence of estimating a Poisson intensity and a positive diffusion drift. Ann. Statist. 30 731-753 (2002) with Genon-Catalot, V, Laredo, C.
  • Asymptotic equivalence for nonparametric regression. To appear in Mathematical Methods of Statistics 11 (2002) No. 1 with Grama, I.
  • A functional Hungarian construction for sums of independent random variables. To appear in Ann. Inst. Henri Poincare', Probability and Statistics, (2002) with Grama, I.
  • AAsymptotic equivalence for a model of independent non identically distributed observations. Submitted, 2003 with Jahnisch, M.
  
  

O     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Oja
  • Estimates of regression coefficients based on the sign covariance matrix with Ollila, Esa, JRSS B(2002) 64, 447-466.
  • Sign and rank covariance matrices with Visuri, Samuli and Koivunen, Visa, Journal of statistical plannings and inference 91(2000), 557-575.
  • Affine invariance multivariate sign and rank tests and corresponding estiamtes: a review Scandianvian J. statistics 1999 26 319-343.
  • On the influence funstins of certain bivariate medians with Ninimaa, A. JRSS B 57(1995) 565-574.
  • Affine invariant multivariate one-sample sign tests with Gettmansperger, T.P. and Nyblom, J. JRSS B 56(1994) 221-234.
  • Affine invariant multivariate multisample sign tests with Hettmanserger, T. JSRR 56(1994) 235-249.
  • On certain bivariate sign tests and medians with Brown, B.M. and Hettmanspeger, T.P. JASA 87(1992)
  • Bivariate sign tests with Nyblom, J. JASA 84(1989)
  • Asymptotic properties of generalized median in the case of multivariate normality with Minimaa, A. JRSS B 47(1985), 372-377.
• Ollila, E.
  • Estimates of regression coefficients based on sign covariance matrix with Thomas P. Hettmansperger and Hannu Oja. Journal of the Royal Statistical Society, Series B, 64, 447-466.
  • Affine equivariant multivariate rank methods with Samuli Visuri, Esa Ollila, Visa Koivunen, Jyrki M?tt?nen and Hannu Oja. Journal of Statistical Planning and Inference, To appear.
  • Estimates of regression coefficients based on lift rank covariance matrix with Hannu Oja and Visa Koivunen. ournal of the American Statistical Assocation, To appear.
  • The Affine equivariant sign covariance matrix: Asymptotic behavior and efficiencies with Hannu Oja and Christophe Croux,
  • Influence function and asymptotic efficiency of the affine equivariant rank covariance matrix with Christophe Croux and Hannu Oja.
  
  

P     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Paris 6
  • Penalized stein methods
  • Optimal aggregation of classifiers by Tsybakov
  • Adaptive prediction by Tsybakov
  • Sharp adaptive estimation
  • From model selection to adaptive estimation
  • Minimum contrast estimatiors on sieves
  • Risk bounds for model selection via penalization
  • An adaptive compression algorithm in Besov space
  • A Dvoretzky-Kiefer-Wofowitz type inequality for the Kaplan-Meiser estimator
  • Model selection for regression on random design by Baraud, Y.
  • About the constans in Talagrand's concentration inequalities for EP
  • Optimal constans for Hoeffding type inequalities
  • Density estimation via exponential model selection by Castellan, G.
  • Gaussian model selection
  • LR inequalities with application to various mixtures
  • Semiparametric deconvolution with unknown variance by Matias, C.
  • Model selection for CART regression trees
  • Tests for convex hypotheses by Baraud, V., Huet, S., and Laurent, B.
• Pollard
  • Paris Lecture
  • Asymptopia
  • Asymptotics for minimisers of convex processes. with Nils Lid Hjort
  • * Some thoughts on Le Cam's statistical decision theory
  • Tusnady's lemma revisited with Andrew Carter
  • * Maximal inequalities via bracketing with adaptive truncation
  • Kim and Pollard
  • LN on elementray probability
    • Probabilities and random variables
    • Expectations
    • Things binomial
    • Variances and covariances
    • Unexpected Symmetry
    • Continuous distributions
    • Normal distribution
    • Poisson approximation
    • Poisson processes
    • Joint densities
    • Conditional densities
    • Multivariate normal
    • Generating functions
    • Chpater 1 to 2
• Portnoy
  • Censored regression quantiles
  • Convergence rates for maximal score estimators in binary response regressions
  • Tail behavior of the LSE
  • A robust joourney in the Millennium
  • Minmax median
• Powell, J.
  • Endogneity in semiparametric binary response models
  • Endogeneity in nonparametric and semiparametric regresson models
  • Pairwise difference estimators for nonlinear models
  • Instgrumental variable estimation of nonparametric models
  • Two-step estimatiion of semiparametric censored regression models
  
  

Q     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Quantaum Statistics
  • Quantum information and computation by Preskill
  • Quantum information and computation (chapter 7) by Preskill
• Lianfen Qian who was Koul's Ph.D. student
  • Asymptotics of M-estimators in two-phase linear regression models with Koul, H.L. and Surgailis, Donatas.
  • Asypmtotics of MLE in a two-phase linear regressionj modelwith Koul, H.L.
  • Estimation of Weibull parametgers for groupted atat with competing risks with Correa, Jose.
  
  

R     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Ren, Jian-Jian
  Second order Hadamard differentiability in statistical application with Sen, P.K. in JMA(2001)
  
  

S     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Serfling
  • papers
  • General notions of statistical depth function
  • Structural properties and converence results for contours of sample statistical depths functions
  • Robust estimation of tail paremeters for two-parameter pareto and exponential models via generalized quantile statistics
  • Robust estimation via generalize L-statistics
  • Nonparametric notions of multivariate scatter measures
  • Generalized quantile processes based on mulitvariate depth funcitons
  • On the performance of some robust nonparamtric location measure
  • A Depth functin and a scale curve based on spatial quantiles
  • Quantile functins for multivaraite analysis
  • On the performance of some robust nonparametric location measures relative to a general notion of multivariate symmetry, Pages 55-79 with Yijun Zuo
  • Genelarize quantile processes based on multivariate depth funcitons, with application in nonparametric multivariate analysis, JMA
  • General notions of statistical depth function AS, 2000
  • Structural properties and convergence results for contours of sample statistical depth functions .
• Schervish at Canegie Mellon
  • Lecture notes on Statistics
  • Exercised
• Schick
  • Unified papers
  • Yu's paper
  • Functional convergence and optimality of plug-in estimators for stationary densities of moving average processes with Wolfgang Wefelmeyer
  • On the construction of efficient estimators in semiparametric models with J. Forrester, W. Hooper, and H. Peng
  • Efficient estimation of linear functionals of a bivariate distribution with equal, but unknown, marginals: The minimum chi-square approach with H. Peng
  • Efficient estimation of linear functionals of a bivariate distribution with equal, but unknown, marginals: The least squares approach 　with H. Peng
  • Estimating linear functionals of the error distribution in nonparametric regression　 with U. U. Mueller, and W. Wefelmeyer
  • Estimating the error variance in nonparametric regression by a covariate-matched U-statistic with U. U. Mueller, and W. Wefelmeyer
  • Root-n consistent and optimal density estimators for moving average processes with W. Wefelmeyer
  • Estimating invariant laws of linear processes by U-statistics with W. Wefelmeyer
  • Testing for superiority among two regression curves with H. Koul
  • Efficient estimation in invertible linear processes with W. Wefelmeyer
  • Efficient estimation in a semiparametric heteroscedastic autoregressive model
  • Estimating the innovation distribution in nonlinear autoregressive models with W. Wefelmeyer
  • On efficient estimation of linear functionals of bivariate distribution with unknown marginals with H. Peng
• Sorensen, Michael
  • Lecture note
• Speed, Terry
  • Incorportationg interference into the linkage analysis of experimental crosses by Nicola Joy Armstrong's thesis
  • Genetic linkage analysis based on identity by descent using Markov chain Monte Carlo sampling on large pedigrees by Alexandre Bureau' thesis 2001 Srping
  • A plasmodium falciparum genfinder by Anthony Wirth's thesis
• Vladimir Spokoiny
  • On larege deviation efficiency in statistical inference with Puhalskii, A.
  • On estimation of the L_r norm of regression function with Lepski, O., and Nemirovski, A.
  • Variance estimation for high-dimensional regression models.
  • Deviation probability bound for martingales with applications to statistical estimation. with Liptser, R.
  • Statistical inference for time-inhomogeneous volality models with Dauilo, M.
  • On estimation of the L_r norm of a regression function with Lepski, O. and Nemirovski, A.
  • Adaptive hypothesis testing using wavelet
  • An adaptive, rate-optimal test of a parametric model against nonparmetric model with Horozitz, J.
  • Structural tests in additive regression with Hardle, W. and Sperlich, S.
  • Data-driven testing the fit of linear models
  • Variance estimation for high-dimensional regression models
  • Estimation of a functional with discontinuities via local polynomial fit with an adaptive window choice
  • Deviation porbability bound and martingales with applications to statistical estimation with Lipster, R.
  • Stucture adaptive approach for dimension reduction with Marian, H. and Anatoli, J. and Jorg, P.
  • Adaptive estimation for a time inhomogeous stochastic-volatitlity model with Hardle, W. and Teyseiere, G.
  • Adaptive drift estimation for nonparametric difusion model
  • Optimao pointwise adaptive methods in nonparametric estimation with Lepski, O.V.
  • An adaptive, rate-optimal test of linearity for median regression model with Worozitz, J.
  • Multiscale testing of qualitative hypothese with Dummbgen, L.
  • On estimating a dynamic function of a stochastic system with averaging with Lipster, R.
  • Direct estimaiton of the index coefficient in a sigle-index model with Marian, H., and Anotoli, J.
  • On large deviation efficiency in statistical inference with Puhalskii, A.
  • Minimax nonparametric hypotheseis testing: the case of an inhomogeneous alternative with Lepski, O.V.
  • Exact asymptotics of minimax Bahadur risk in LIpschitz regression with Korostelev, A.P.
  • Semiparametric single index vesus fixed link function model with Hardle, W. and Sperlich, S.
  • A new variable bandwidth selection for kernel estimation
  • Estimation of a fucntion with discontinuities via local polynomial fit with an adaptive window choice
  • On estimation of non-smooth functionals with Lepski, O. and Nemirovski, A.
  • Optimal choice of observation window for Poisson observations with Kutoyants, Y.
  • Testing a linear hypothesis using Haar transformation
  • Image denoising: pointwise adaptive approach
  • MOderate deviations type evaluation for integral functionals of diffusion processes with Lipster, R.
  • On estimating a dynamic function of stochastic system with averaging with Lipster, R.
  • Otimal nonparametric testing of qualitative hypotheses with Dumbgen, L.
  • Direct estimation of the index coefficients in a single-index model with Marian, H. and Anatoli, J>
  • Data-driven testing the fit of linear models
  • Deviatoin probability bound for martingales with applications to statistical estimation with Lipster, R.
  • Variance estimation for high-dimensionalregression models
  • Direct estimation for nonparametric diffusion model; nonasymptotic approach
  • Vector adaptive weights smoothing with application to MRI Polizehl, J.
  • An adaptive,rate-optimal test of a parametric model against a nonparametric alternative with Horowitz, J.
  • Structural tests in additive regression with Hardle, W., and Sperlich, S.
  • Structure adaptive approach for dimension reduction with Hristache, M., Juditsky, A. and Polzehl, J.
  • Statistical inference for time-inhomogeneous volatility models with Mercurio, D.
  • Transitin density estimation for stochastic differential equations via forward-reverse representations with Milstein, G.M. and Schoenmarkers, J.G.M.
  • Confidence estiamtion of the covariance function of statinary and locally stationary processes with Mihai, G.
  • An adaptive, rate-optimal test of linearity for median regression model with Horowitz, J.
• Stone
  • Functional ANOVA modelling for proportinal hazards models with Jianhua, H. Kooperberg, K. and Troug, Y.K.
  • The L_1 rate of convergence for event history regression with time-dependent covariates with Huang, J.Z.
  • Polynomial splines and their tensor products in extended linear models with Hansen, M., Kooperberg, C., and Troug, Y.K.
  • The use of polynomilal spline and their tensor products in multivariate estimation
  • Asymptotics for doubly flexible logspline response models
  • fonparametric function estimation involving time series
  • Large-sample inference for log-spline models
  • The dimensionality reduction principle for generalized additive models
  • Additive regression and other nonparametric models
  • An asymptotically iptimal window selction rule for kernel density estimates
  • Optimal global rates of convergence for nonparametric regression
  • Admissible selection of an accurate and parsimoniou normal linear regression model
  • Optimal rates of convergence for nonparametric regression
  • consistent nonparametric regression
• JIAYANG SUN at Case Western University
  • Confidence bands in generalized linear modelswith Loader, C.
  • Confidence bands for growth and response curves with Raz, J. and Faraway, J.
  • Semi-parametric estimates under biased sampleing with Woodroofe, M.
  • Tests in projectin pursuit regression with Park, M.G.
  • Robustness of tube formulta based confidence bands with Loader, C.
  • Testing uniformity versus a monotone density with Woodroof, M.

T     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Michael Talagrand
  • Isoperimetry and processes in probability in Banach space
  • Mean field models for spin glasses: a first course
    • Chapter 1
    • Chapter 2
    • Chapter 3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapter 7
    • Chapter 8
    • References
• Tanaka, Katsuto
  • 共和分分析
  • ウェーブレット解析
  • フラクショナル ARIMA モデルに関する統計理論
  • 非定常経済時系列におけるトレンドの統計的問題
  • 非定常および反転不可能な時系列モデルに関する統計的推測
• Tanizaki, Hisashi
  • Nonlinear filters: Estimation and applications, Springer
    • Chapter 0-3
    • Chapter 4
    • Chapter 5
    • Chapter 6
    • Chapter 7-8
  • (上級)統計学
    • 講義録
    • 講義録(英語）
    • 講義録(演習問題)
  • 計量経済学講義録
  • 計量経済学講義録(大学院)
  • カルマン・フィルター・モデルの理論と経済学への応用
  • State-Space model in linear case
• Rob Tibshirani
  • Pre-validation and inference in microarrays with Bradley Efron, Statistical Applications in Genetics and Molecular Biology, Vol 1, No. 1, 2002.
  • Class prediction by nearest shrunken centroids, with applications to DNA microarrays with Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu.
  • Least angle regression with Brad Efron, Trevor Hastie, Iain Johnstone.
  • Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. PNAS 98: 10869-10874. with Therese Sorlie, Perou, C., Robert Tibshirani, Turid Aas, Stephanie Geisler, Hilde Johnsenb, Trevor Hastie, Michael B. Eisenh, Matt van de Rijn, Stefanie S. Jeffrey, Thor Thorsen, Hanne Quist, John C. Matese, Patrick O. Brown, David Botstein, Per Eystein Lonninngg, and Anne-Lise Borresen-Daleb.
  • Microarrays, Empirical Bayes Methods, and False Discovery Rates with Bradley Efron, John Storey
  • Cluster validation by prediction strength with Guenther Walther, David Botstein and Pat Brown
  • Exploratory screening of genes and clusters from microarray experiments with Trevor Hastie, Balasubramanian Narasimhan, Michael Eisen, Gavin Sherlock, Pat Brown, and David Botstein
  • Significance analysis of microarrays applied to the ionizing radiation response. with Virginia Tusher, Robert Tibshirani and Gilbert Chu
  • Statistical challenges in the analysis of DNA microarray data, Lecture delivered to National Academy of Sciences, November 2000.
  • Microarrays and Their Use in a Comparative Experiment with Bradley Efron, Robert Tibshirani, Virginia Goss and Gilbert Chu
  • Supervised Harvesting of Expression Trees with Trevor Hastie, Robert Tibshirani, David Botstein and Pat Brown.
  • Estimating the number of clusters in a dataset via the Gap statistic with Robert Tibshirani, Guenther Walther
  • Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling with Alizadeh, A. and 23 others., Nature 403, 503-511 (2000)
  • Gene Shaving: a New Class of Clustering Methods for Expression Arrays with Trevor Hastie, Robert Tibshirani, Michael Eisen, Pat Brown, Doug Ross, Uwe Scherf, John Weinstein, Ash Alizadeh, Louis Staudt, David Botstein
  • Clustering methods for the analysis of DNA microarray data with Hastie, T. Eisen, M., Ross, D. , Botstein, D. and Brown, P
  • The global pairwise approach to radiation hydrid mapping with Lazzeroni, L. and Hastie, T. and Olshen, A. and Cox, D.R.
  • Additive Logistic Regression: a Statistical View of Boosting with Friedman, J., Hastie, T.
  • Learning from Data: Statistical Advances and Challenges, Plenary lecture, Center for automated learning and discovery, CMU, June 13, 1998.
  • Bayesian backfitting with Hastie, T.
  • The covariance inflation criterion for model selection with Knight, K.
  • Some thoughts from half a career in statistics
  • The out-of-bootstrap method for model averaging and selection with Rao, J.S.
  • A comparison of statistical learning methods on the GUSTO database with Ennis, Hinton, Naylor, Revow.
  • The Problem of Regions with Efron, B.
  • Classification by pairwise coupling with Hastie, T.
  • Who is the fastest man in the world?, Revised January, 1997. To appear, American Statistician.
  • Two applications of the bootstrap, Gordon Ashton Memorial Lecture, Guelph, Sep 17 1996.
  • Cellular telephones and automobile collisions: some variations on matched case-control analysis with Redelmeier, D.
  • Computer-aided diagnosis of mammographic masses with Hastie, T., Ikeda, D., much smaller version without the images. It has been accepted for publication in J. Comp. and Graph. Statistics.
  • Bias, variance and prediction error for classification rules
  • Model search and inference by bootstrap bumping with Knight, K., J. Comp. and Graph. Statistics.
  • Cross-Validation and the Bootstrap: Estimating the Error Rate of a Prediction Rule with Efron, B.
  • Discriminant Adaptive Nearest Neighbor Classification.
  • A comparison of some error estimates for neural network models
  • Coaching variables for regression and classification with Hinton, G.E.
  • Regression selection and shrinkage via the lasso
  • Discriminant Analysis by Gaussian Mixtures with Hastie, T.
  • A proposal for variable selection in the Cox model
  • Penalized Discriminant Analysis. with Hastie, T. J., Buja, A.
  • Flexible Discriminant Analysis by Optimal Scoring with Hastie, T. J., Tibshirani, R. and Buja, A.
  • Principal curves revisited., Stat and computing 1992.
• Tukey
  • Special issue on Tukey

U     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• U statistics
  • Bickel
  • Callaert
  
  

V     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Van der Vaart
  • Infinite Dimensional M-estimation
  • On a model o fIbragimov and Hasminskii
  • Semiparametric mixtures in a case-control studies
  • On proflie likelihood
  • Rates of convergence of posteriro distributions
  • Censoring and passive regression
  • Asymptotic distribution v-values
  • Preservation of Glivennko-Cantelli Theorems
  • Current status regression
  • Direhlet mixtures of normals
  • Testing monotonicity of regression
  • Semiparametric Statistics (Ecole of Ete de Saint-Flour 1999
• 　Varadhan
  • Lecture Note on Probability
  • Lecture Note on Stochastic Processes
  • Lecture Note on Statistics
  • Lecture Note on Finance
  • Lecture Note on Harmonic Analysis
  • Lecture Note on Stochastic Processes by Alan Bain
  • An introduction to Mallvian caluculas by Oksendel
  • Lecture Note on Complex analysis
  • A beginer's guide to credit derivatives by Vaillant, N.
  • Stochastic calculus for finance
  • Stochastic calculus and finance by Steven Shreve
• Nicolas VAYATIS
  • On the Bayes-risk consistency of boosting methods, with G. Lugosi
  • Exact Rates in Vapnik-Chervonenkis Bounds To be published in Annales de l'IHP - Probabilite's et Statistiques.
  • The Role of Critical Sets in Vapnik-Chervonenkis Theor Proceedings of COLT'2000, Stanford University.
  • Ine'galite's de Vapnik-Chervonenkis et mesures de complexite PhD thesis, Ecole Polytechnique, Palaiseau, France. In English.
  
  

W     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Wagkamp
  • Model selection in nonparametric regression
  • Consistency for the LSE in non-parametric regresion with Sara van de Geer
  • Weighted minimum mean-sqare distance from independence estimation with Brown, Donald J.
  • Two-stage model selection procdures in partially linear regression with Bunea, F.
  • Model selection for regression on a fixed design by Baraud, Y.
  • Bandwidth Selection for Local Linear Regression. Journal of the Royal Statistical Society, Series B 64 (4), 1 - 14 (2002). with * Nicolas Hengartner, Eric Matzner-Lober
• Wahba
  • Gu's paper
  • wahaba1
  • wahba2
  • wabha3
  • whaba4
  • wahba5
  • wahba6
  • wahba7
  • wang's paper
  • Optimal spine smoothing o fMRI time series by generaliezed cross-validation with Carew, J.D,, Xie, W., Nordheim, E.V.
  • Using smoothing spline ANOVA to examine the relation of risk factors to the incidence and progression of Diahetic Reinonathy with Wang, Y., Gu, C., Klein R. and Klein, E.
  • Quantiitaive study of smoothing spline-ANOva based fingerprit methods for attribution of glaoba warming with Chaing,A., Tribbia, J. and Johson, D.R.
  • GRKPACK: fitting smoothing spline ANOVA models for exponential families by Wang, Yuedong
  • The biase-variance tgradeoff and randomized GACV with Lin, X, Gao, F, and Xiang, D.
  • Odds ratio estimation in Bernoulli smoothing splien ANOVA model by Yuedon Wang
  • Support vectore machines for classificaiton in nonstandard situations with Lin, Y. and Yoonkyung Lee
  • Penalized log likelihood densitgy estimation, via smoothing ANOVA and ranGACV- comments to Hansen and Kooperberg `Spline adaptation in extended linear models` with Lin, Y. and Leng, C.
  • Discussion of `Smoothing spline models for tha analysis of nested and crossed samples of curves` by Brumback and Rice with Wang, Y.
  • Backfitting in smoothing spline ANOVA by Luo Zhen
  • Adaptive tuning, four dimensional variational data assimilation, and representers in RKHs
  • Adaptive tuning of numerical weather prediction methods: simultaneous estiamtion of weighting, smoothing and physical parametrs with Gong, J., and Johnson, D.R.
  • Comments to Chong Gu, `Model indexing and smoothing parameter selection in nonparametric function estimation
  • Mixed-effect smoothing spline ANOVA by Wang Yuedong
  • Generalization and regularization in nonlinear learning systems
  • Soft classification, a.k.a. Risk estimation, via penalized log likelihood and smoothing spline analysis of variance with Gu, C., Wang, Y. and Chappell, R.
  • Statistical prperties and adaptive tuning of SVM with Lin, Y. Zhang, H. and Lee, Y.
  • Classification of multliple cancer types by multicategory SMV using gene expression data by Lee Yoonkyung and Lee Choel-Koo
  • On the relatin between the GACV and Joachims' \eta \alpha method for tuning SVM with extensikons to the non-standart case by Lin, Y, Lee, Y. and Zhang, H.
  • Bootstrap confidence interval for smoothing splines and their comparison to Bayesian confidence interval with Wang, Y.
  • A generalized approximate cross validation for smoothing splines wtih non-Gauusian data with Xiang, D.
  • Hybird adaptive splines with Luo, Z.
  • Behaviour near zero of the distribution of GCV smoothing parameter estimates with Wang, Y.
  • Generalization and regularization in nonlinear learning systems
  • Spartial-temporal analysis of temperature using smoothing spline ANOVA with Luo, Z. and Johnson, D.R.
  • Mulicategory SVM with Lee, Y. and Lin, Y.
  • Iterated ranGACV: a computational proxy for the comparative Kullback-Leibler distance by Gao, F.
  • Smoothing splines in nonparametric regression
  • Smoothing splien ANOVA models for large data sets with Bernoulli observatons and the randomized GACV with Lin, X., Xiang, D., Gao, F., Klain, R.
  • Structured machine learnign for soft classification with smoothing spline ANOVA and stacked tuning, testing evaluation with Wang, Y., Gu, C., Klein, R. and Klein, B.
  • Testing the generalized linear model null hypothesis vsrsus smooth alternatives with Xiang, D.
  • Smoothing spline ANOVA with component-wise Bayesian confidence intervals with Gu, C.
  • Multicategory SVM by Lee, Y., Lin, Y.
  • Adaptive tuning of numerical wether prediction models: Part I: randomized GCV and related methods in three and four dimensional data assimilation with Johnson, D.R., Gao, F., and Gong, J.
  • Smoothing spline analysis of variance for polychotomous response data by Xiwu Lin (dissertation)
  • Smoothing spline models with correlated random errors
  • Smoothing spline ANOVA for exponential families, with application to the Wisconsin epidemilogical study of diabetic retinopathy with Wang, Y., Gu, C. Klein, R., and Klein B.
  • On combining data from multiple sources with unkonwn relative weights by Feng Gao
  • Smoothing spline ANOVA fits for very large, nearly regular data sets, with application to historical global climate data (rev) with Luo, Z.
  • SVM, RKHS and randomoized GACV
  • Smoothing spline ANOVA for multivariate Bernoulli observations, with application to ophthalmogogy data with Gao, F, Klein, R., and Klein, B.
  • Generalized approximate cross validatin for SVMs, or another way to look at margin-like wquatities with Lin, Y. and Zhang, H.
  • Optimal properties and adaptive tuning of standard and nonstandard SVM with Lin, Y., Lee., Y. and Zhang, H.
  • Approximate smoothign spline methods for large data sets in the binary case with Xiang, D.
  • Dissertation
    • Backfitting in smoothing spline ANOVA, with application to historical global temperature data by Zhen Luo, 1996
    • Model fitting and testing for non-Gaussian data with large data sets by Xiang Dong (dissertation), 1996
    • Penalized multivariate logistic regression with larege data sets by Fangyu Gao (dissertation), 1999.
    • Nonparametric variable selection and model building via likelihood basis pursuitby Hao Helen Zhang (disertation), 2002
    • Nonparamteric variable selection and model building via likelihood basis pursuit by Hao Helen Zhang (dissertation), 2002.
• Wang, Jane Jing at Univ. of California at Davis
  • Inference for mean difference in the two-sample random censored model, JMA
  • Asymptotic properties of M-estimators based on estimating equations and censored data, J. Scand. Sattist.
• Wand, Matt
  • some paper
• 　Wasserman at Canegimeron
  • Lecture note on Statistics
  • Note on R
  • Problems
  • Lecture note on Nonparametric
• Wellner, Jon
  • Profile Likelihoood
  • Z - estimators and efficient estimators
  • 581(Notes and exercises)
  • Lecture note on empirical processes
  • Dissertation: Likelihood ratio inference in regular and non-regular problems
  • 581-582-583　講義録
  • Likelihood ratio, score, andn Wald statistics in models with monotone functions: some comparision with Moulinath Banerjee
  • Confidence intervals for current status data with Moulinath Banjerjee
  • Estimation with univariate mixed case interval censored data Shunjguang Song
  • An upper bound for uniform entropy nunbers
  • Large sample theory for semiparametric regression models with two-phase, outcom dependent sampling with Norman Breslow and Brad McNeney
  • Information bounds for regression models with missing data with Bin Nan and Mary Emond
  • Estimation of a convex function with Piet Broenenboom and Geurt Jongblood
  • Consistency of semiparametric MLEs for two-phase, outcome dependent data with Brad McNeney and Aad van der Vaart
  • A canonical process for estimation of convex function with Piet Groeneboom, Geurt Jongbloed
  • Preservation theorems for Glivenko-Cantelli and unimform Glivenko-Cantelli classes with Aad van der Vaart
  • Computing Chernoff's distribution with Piet Groeneboom
  • Application of convolution theorems in semiparametric models with non-i.i.d data with Brad McNeney
  • Special topics course 593C Nonparametric estimation for inverse problems algorithms and Asymptotics given by Piet Groeneboom
  • Integrated Brownian motion, conditioned to be positive with Piet Groeneboom and Geurt Jongblood
  • Interval censored survival data: a review of recent progess with Jian Huang
  • Boostrapping Z-estimators with Yihui Zhan
  • Uniform convergence in some limit theorems for multiple particle systems with Evarist Gine
  • Necessary and sufficient conditions for weak consistency of the median of independent but not identically distributed random variables with Ivan Mizera
  • On the distribution of Brownian areas
  • Efficient estimation in the bivariate normal copula model with Chris Klaassen
  • Double censoring: characterization and computation of NMLE with Yihui Zhan
  • Efficient estimation for the Cox model with interval censoring by Jian Huang
  • Information bounds for Cox regression models with missing data. Bin Nan, Mary Emond, and Jon A. Wellner. May 2002
  • Likelihood ratio, score, and Wald statistics in models with monotone functions. Moulinath Banerjee and Jon A. Wellner. Submitted to Bernoulli, April 2002.
  • On longest increasing subsequences and random Young tableaux: experimental results and recent theorems. Jon Wellner. Posted 4/5/2002. Data posted 4/5/2002.
• Michael Wolf
  • Some Hypothesis Tests for the Covariance Matrix when the Dimension is Large Compared to the Sample Size with Olivier Ledoit
  • Subsampling Inference in Threshold Autoregressive Models with Jesu's Gonzalo
  • Flexible Multivariate GARCH Modeling with an Application to International Stock Markets wiht Olivier Ledoit , Pedro Santa-Clara
  • Improved Estimation of the Covariance Matrix of Stock Returns With an Application to Portofolio Selection with Olivier Ledoit
  • Subsampling the Mean of Heavy-Tailed Dependent Observations with Piotr Kokoszka
  • Improved Nonparametric Confidence Intervals in Time Series Regressions with Joseph P. Romano
• Wong's paper
• Woodroofe
  • 確率論の講義録

X     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Xie, Ming at Department of Statistics, Ruters University
  • Asymptotics for Generalized Estimating Equations with Large Cluster Sizes. with Yang, Y.-N. to appear in AS.
• XploRE
  • Partial linear model
  • Wavelet approximation and statistical application
  • Non- and semi-parametric modeling
  • Applied nonparametric regressionn
  • Empirical processes
  • Semiparametric extension to GLM's
  • Measuring risk in complex system
  • Introduction to Econometrics
  • Risk theory
  • Robust sequential analysis

Y     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Yang
  • Unified papers
  • Information-Theoretic Determination of Minimax Rates of Convergence with Andrew Barron
  • Nonparametric Regression with Dependent Errors
  • On Adaptive Function Estimation
  • Model Selection for Nonparametric Regression
  • Minimax Nonparametric Classification - Part I: Rates of Convergence
  • Minimax Nonparametric Classification - Part II: Model Selection for Adaptation
  • Combining Different Procedures for Adaptive Regression
  • Adaptive Estimation in Pattern Recognition by Combining Different Procedures
  • Nonparametric Regression with Correlated Errors with OPSOMER, Jean and Yuedong WANG
  • Regression With Multiple Candidate Models: Selecting or Mixing?
  • Adaptive Regression by Mixing
  • Aggregating Regression Procedures for a Better Performance
  • Minimax Rate Adaptive Estimation Over Continuous Hyper-Parameters
  • Combining Forecasting Procedures: Some Theoretical Results
  • Randomized allocation with nonparametric estimation for a multi-armed bandit problem with covariates with Dan Zhu
  • Regression with multiple candidate models: selecting or mixing?
  • Combining time series models for forecasting, revised for International Journal of Forecasting with Hui Zou
• Yang at Michigan State Univesity
  • Papers
• Yu, Bin
  • Analysing Bagging with Buhlmann, P.
  • Boosting with L_2-loss: regression and classification with Buhlmann, P.
  • Wavelet thresholding via MDL for natural images with Hansen, M.
  • Model selection and the priciple of MDL with Hansen, M.H>
  • MDL model selection criteria for GLM with Hansen, M.
  • Wavelet thresholding for multiple noisy image copies with Chang, S.G. and Vetterfi, M.
  • Iterated logarithmic expansion of pathwise code lengths for exponential families with Li, L.
  • Microarray images compression: SLOCO and the effect of information loss with Jornsten, R., Wang, W. and Ramchandran, K.
  • Papers
  
  

Z     A B C D E F G H I J K L M N O P Q R S T U V W X Y Z Top

• Ruben H. Zamar
  • Lecture note on robust inference
  • Lecture note on elementray statistics
• Zhan
  • Zhan の参考文献
• Zhang, Cun-Hui at at Department of Statistics, Ruters University
  • A robust clustering ... data depth 　(with R. Jornstern and Y. Vardi)
  • L-1 depth, depth relative to model ... regression. (with X. Tian and Y. Vardi)
  • Two-sample tests for growth curves under ... censoring (with Y. Vardi and Z. Ying)
  • Efficient estimation of sums of random variables
  • General empirical Bayes wavelet methods
  • Least squares estimators of the mode of a unimodal regression function (with J.-M. Shoung)
  • Risk bounds in isotonic regression
  • NPMLE of a probability measure with coarse data in sigma-VC Helly classes
  • Asymptotic equivalence theory for nonparametric regression ... (with L.D. Brown, T.T. Cai and M.G. Low)
  • Consistency of nonparametric maximum likelihood estimators of a distribution with set censored data
  • Coupling inequalities for some random design metrices ... (with L.D. Brown)
• Zhang
  • Unified papers
• Linda Zhao
  • Free knot polynomial spline confidence intervals (with W. Mao)
  • Direct asymptotic equivalence of nonparametric regression and the infinite dimensional location problem (with L.D. Brown)
  • Sharp adaptive estimation by a blockwise method (with T.T. Cai and M.G. Low)
  • Statistical equivalence at suitable frequencies of GARCH and stochastic volatility models with the corresponding diffusion model (with L.D. Brown and Y. Wang)
  • Tradeoffs between global and local risks in nonparametric function estimation (with T. Cai and M. Low)
  • Bayesian nonparametric point estimation under a conjugate prior (with X. Li) To appear in Statistics and Probability Letters.
  • A new test for the Poisson distribution (with L. Brown) To appear in Sankhya
  • Nonparametric tests for the mean of a non-negative population (with W. Wang) To appear in J. Statist. Planning and Inference.
• James, V. Zidek
  • The weighted likelihood with F. Hu, Candian J. Statistics, vol. 20(2002), 343-369
• Joel Zinn
  • Weighted uniform consistency of kernel density estimators
  • Consistent sepcification tests for semiparametric/nonparametric models based on series estimation methods
  • Extremal properties of Rademacher functions with application to Khintchine and Rosenthal inequality
  • Necessary condition for the bootstrap of the mean with Gine
  • A converse exponential inequality
  • Exponential and moments of inequalities for U-statistics
  • Gaussian characterization of uniform Donsker classes of functions
  • Bootstrapping general empirical measures
  • Necessary and sufficient conditions for the strong laws of large numbers for U-statistics
  • On Hoffmann-Jorgensen's inequality for U-processes
  • Notes for Math 415, Fall, 1995 (Algebra)
  • A remark on convergence in distribution of U-statistics
  • Laws of large numbers for quadratic forms, maxima of products and truncated sums of LLD random variables
  • The LIL for canonical U-statistics of order 2
• Zuo, Yijun at Arizona State University
  • Some Quantitative Relationships Between Two Types of Finite Sample Breakdown Point. Statistics and Probability Letters. 51 (4): 369-375, 2001
  • Nonparametric Multivariate Notions of `Scatter' and `More Scattered' Based on Statistical Depth Functions''. Journal of Multivariate Analysis. 75: 62-78, 2000 with Serfling
  • Finite sample tail behvior of multivariate trimmed mean besed on Tukey-Donoho halfspace depth, Metrika. 52 (1): 69-75, 2000
  • Structural Properties and Convergence Results for Contours of Sample Statistical Depth Functions. The Annals of Statistics. 28(2): 483-499, 2000 with Serfling
  • A Note on Finite Sample Breakdown Points of Projection Based Multivariate Location and Scatter Statistics, Metrika. 51: 259-265, 2000
  • General Notions of Statistical Depth Function. The Annals of Statistics. 28(2): 461-482, 2000 with Serfling
  • On the Performance of Some Nonparametric Location Measures Relative to a General Notion of Multivariate Symmetry. Journal of Statistical Planning and Inference. 84: 55-79, 2000 with Serfling
  • Multivariate Monotone Location Estimators, Sankhya Series A. 62(2): 161-177, 2000
  • Finite sample tail behavior of multivariate location estimators, Journal of Multivariate Analysis. (to appear)
  • Projection based depth functions and associated medians. The Annals of Statistics. (to appear)
  • Influence function and maximum bias of projection depth based estimators, The Annals of Statistics. (tentatively accepted)