2004

最新文章

• 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...
• Consistent covariate selection and post model selection inference in semiparametric regression AI翻译
  作者：Bunea, F
  作者单位：State University System of Florida; Florida State University
  摘要：This paper presents a model selection technique of estimation in semiparametric regression models of the type Y-i = beta'X-i + f (T-i) + W-i, i = 1,..., n. The parametric and nonparametric components are estimated simultaneously by this procedure. Estimation is based on a collection of finite-dimensional models, using a penalized least squares criterion for selection. We show that by tailoring the penalty terms developed for nonparametric regression to semiparametric models, we can consistentl...
• Information bounds for Cox regression models with missing data AI翻译
  作者：Nan, B; Emond, M; Wellner, JA
  作者单位：University of Michigan System; University of Michigan; University of Washington; University of Washington Seattle
  摘要：We derive information bounds for the regression parameters in Cox models when data are missing at random. These calculations are of interest for understanding the behavior of efficient estimation in case-cohort designs, a type of two-phase design often used in cohort studies. The derivations make use of key lemmas appearing in Robins, Rotnitzky and Zhao [J. Amer Statist. Assoc. 89 (1994) 846-866] and Robins, Hsieh and Newey [J. Roy. Statist. Soc. Ser. B 57 (1995) 409-424], but in a form suited...
• On the forward and backward algorithms of projection pursuit AI翻译
  作者：Zhu, M
  作者单位：University of Waterloo
  摘要：This article provides a historic review of the forward and backward projection pursuit algorithms, previously thought to be equivalent, and points out an important difference between the two. In doing so, a small error in the original exploratory projection pursuit article by Friedman [J Amer. Statist. Assoc. 82 (1987) 249-266] is corrected. The implication of the difference is briefly discussed in the context of an application in which projection pursuit density estimation is used as a buildi...