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作者：Paindaveine, Davy; Remy, Julien; Verdebout, Thomas
作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles
摘要：We consider the problem of testing, on the basis of a p-variate Gaussian random sample, the null hypothesis H-0 :( )theta(1) = theta(0)(1) against the alternative H-1 : theta(1) not equal theta(0)(1), where theta(1) is the first eigenvector of the underlying covariance matrix and theta(0)(1) is a fixed unit p-vector. In the classical setup where eigenvalues lambda(1) > lambda(2) >= ... >= lambda(p) are fixed, the Anderson (Ann. Math. Stat. 34 (1963) 122-148) likelihood ratio test (LRT) and the...
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作者：Xu, Min; Jog, Varun; Loh, Po-Ling
作者单位：Rutgers University System; Rutgers University New Brunswick; University of Wisconsin System; University of Wisconsin Madison
摘要：Community identification in a network is an important problem in fields such as social science, neuroscience and genetics. Over the past decade, stochastic block models (SBMs) have emerged as a popular statistical framework for this problem. However, SBMs have an important limitation in that they are suited only for networks with unweighted edges; in various scientific applications, disregarding the edge weights may result in a loss of valuable information. We study a weighted generalization o...
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作者：Goes, John; Lerman, Gilad; Nadler, Boaz
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Weizmann Institute of Science
摘要：Estimating a high-dimensional sparse covariance matrix from a limited number of samples is a fundamental task in contemporary data analysis. Most proposals to date, however, are not robust to outliers or heavy tails. Toward bridging this gap, in this work we consider estimating a sparse shape matrix from n samples following a possibly heavy-tailed elliptical distribution. We propose estimators based on thresholding either Tyler's M-estimator or its regularized variant. We prove that in the joi...
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作者：Li, Xinran; Ding, Peng; Rubin, Donald B.
作者单位：University of Pennsylvania; University of California System; University of California Berkeley; Pennsylvania Commonwealth System of Higher Education (PCSHE); Temple University
摘要：With many pretreatment covariates and treatment factors, the classical factorial experiment often fails to balance covariates across multiple factorial effects simultaneously. Therefore, it is intuitive to restrict the randomization of the treatment factors to satisfy certain covariate balance criteria, possibly conforming to the tiers of factorial effects and covariates based on their relative importances. This is rerandomization in factorial experiments. We study the asymptotic properties of...
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作者：Bachoc, Francois; Preinerstorfer, David; Steinberger, Lukas
作者单位：Universite de Toulouse; Universite Toulouse III - Paul Sabatier; Universite Libre de Bruxelles; University of Freiburg
摘要：We suggest general methods to construct asymptotically uniformly valid confidence intervals post-model-selection. The constructions are based on principles recently proposed by Berk et al. (Ann. Statist. 41 (2013) 802-837). In particular, the candidate models used can be misspecified, the target of inference is model-specific, and coverage is guaranteed for any data-driven model selection procedure. After developing a general theory, we apply our methods to practically important situations whe...
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作者：Zhu, Guangyu; Su, Zhihua
作者单位：University of Rhode Island; State University System of Florida; University of Florida
摘要：Sparse partial least squares (SPLS) is widely used in applied sciences as a method that performs dimension reduction and variable selection simultaneously in linear regression. Several implementations of SPLS have been derived, among which the SPLS proposed in Chun and Keles (J. R. Stat. Soc. Ser. B. Stat. Methodol. 72 (2010) 3-25) is very popular and highly cited. However, for all of these implementations, the theoretical properties of SPLS are largely unknown. In this paper, we propose a new...
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作者：Bravo, Francesco; Carlos Escanciano, Juan; Van Keilegom, Ingrid
作者单位：University of York - UK; Universidad Carlos III de Madrid; KU Leuven
摘要：In both parametric and certain nonparametric statistical models, the empirical likelihood ratio satisfies a nonparametric version of Wilks' theorem. For many semiparametric models, however, the commonly used two-step (plug-in) empirical likelihood ratio is not asymptotically distribution-free, that is, its asymptotic distribution contains unknown quantities, and hence Wilks' theorem breaks down. This article suggests a general approach to restore Wilks' phenomenon in two-step semiparametric em...
