The Annals of Statistics

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• Sufficient burn-in for Gibbs samplers for a hierarchical random effects model AI翻译
  作者：Jones, GL; Hobert, JP
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; University of Florida
  摘要：We consider Gibbs and block Gibbs samplers for a Bayesian hierarchical version of the one-way random effects model. Drift and minorization conditions are established for the underlying Markov chains. The drift and minorization are used in conjunction with results from J. S. Rosenthal [J. Amer. Statist. Assoc. 90 (1995) 558-566] and G. O. Roberts and R. L. Tweedie [Stochastic Process. Appl. 80 (1999) 211-229] to construct analytical upper bounds on the distance to stationarity. These lead to up...
• Testing predictor contributions in sufficient dimension reduction AI翻译
  作者：Cook, RD
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
  摘要：We develop tests of the hypothesis of no effect for selected predictors in regression, without assuming a model for the conditional distribution of the response given the predictors. Predictor effects need not be limited to the mean function and smoothing is not required. The general approach is based on sufficient dimension reduction, the idea being to replace the predictor vector with a lower-dimensional version without loss of information on the regression. Methodology using sliced inverse ...
• Semiparametric density estimation by local L2-fitting AI翻译
  作者：Naito, K
  作者单位：Shimane University
  摘要：This article examines density estimation by combining a parametric approach with a nonparametric factor. The plug-in parametric estimator is seen as a crude estimator of the true density and is adjusted by a nonparametric factor. The nonparametric factor is derived by a criterion called local L-2-fitting. A class of estimators that have multiplicative adjustment is provided, including estimators proposed by several authors as special cases, and the asymptotic theories are developed. Theoretica...
• Three papers on boosting: An introduction AI翻译
  作者：Koltchinskii, V; Yu, B
  作者单位：University of New Mexico; University of California System; University of California Berkeley
• On the Bayes-risk consistency of regularized boosting methods AI翻译
  作者：Lugosi, G; Vayatis, N
  作者单位：Pompeu Fabra University; Sorbonne Universite; Universite Paris Cite
  摘要：The probability of error of classification methods based on convex combinations of simple base classifiers by boosting algorithms is investigated. The main result of the paper is that certain regularized boosting algorithms provide Bayes-risk consistent classifiers under the sole assumption that the Bayes classifier may be approximated by a convex combination of the base classifiers. Nonasymptotic distribution-free bounds are also developed which offer interesting new insight into how boosting...
• Smooth discrimination analysis (vol 27, pg 1808, 1999) AI翻译
  作者：Mammen, E; Tsybakov, AB
  作者单位：University of Mannheim; Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Paris Cite; Sorbonne Universite
• Breakdown points for maximum likelihood estimators of location-scale mixtures AI翻译
  作者：Hennig, C
  作者单位：University of Hamburg
  摘要：ML-estimation based on mixtures of Normal distributions is a widely used tool for cluster analysis. However, a single outlier can make the parameter estimation of at least one of the mixture components break down. Among others, the estimation of mixtures of t-distributions by McLachlan and Peel [Finite Mixture Models (2000) Wiley, New York] and the addition of a further mixture component accounting for noise by Fraley and Raftery [The Computer J. 41 (1998) 578-588] were suggested as more robus...
• Densities, spectral densities and modality AI翻译
  作者：Davies, PL; Kovac, A
  作者单位：University of Duisburg Essen
  摘要：This paper considers the problem of specifying a simple approximating density function for a given data set (x(1),.... x(n)). Simplicity is measured by the number of modes but several different definitions of approximation are introduced. The taut string method is used to control the numbers of modes and to produce candidate approximating densities. Refinements are introduced that improve the local adaptivity of the procedures and the method is extended to spectral densities.
• Bounds on coverage probabilities of the empirical likelihood ratio confidence regions AI翻译
  作者：Tsao, M
  作者单位：University of Victoria
  摘要：This paper studies the least upper bounds on coverage probabilities of the empirical likelihood ratio confidence regions based on estimating equations. The implications of the bounds on empirical likelihood inference are also discussed.
• Robust nonparametric inference for the median AI翻译
  作者：Yohai, VJ; Zamar, RH
  作者单位：University of Buenos Aires; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET); University of British Columbia
  摘要：We consider the problem of constructing robust nonparametric confidence intervals and tests of hypothesis for the median when the data distribution is unknown and the data may contain a small fraction of contamination. We propose a modification of the sign test (and its associated confidence interval) which attains the nominal significance level (probability coverage) for any distribution in the contamination neighborhood of a continuous distribution. We also define some measures of robustness...