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作者：Ledoit, Olivier; Wolf, Michael
作者单位：University of Zurich
摘要：Many statistical applications require an estimate of a covariance matrix and/or its inverse. When the matrix dimension is large compared to the sample size, which happens frequently, the sample covariance matrix is known to perform poorly and may suffer from ill-conditioning. There already exists an extensive literature concerning improved estimators in such situations. In the absence of further knowledge about the structure of the true covariance matrix, the most successful approach so far, a...
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作者：Roman, Jorge Carlos; Hobert, James P.
作者单位：Vanderbilt University; State University System of Florida; University of Florida
摘要：Bayesian analysis of data from the general linear mixed model is challenging because any nontrivial prior leads to an intractable posterior density. However, if a conditionally conjugate prior density is adopted, then there is a simple Gibbs sampler that can be employed to explore the posterior density. A popular default among the conditionally conjugate priors is an improper prior that takes a product form with a flat prior on the regression parameter, and so-called power priors on each of th...
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作者：Vogt, Michael
作者单位：University of Cambridge
摘要：In this paper, we study nonparametric models allowing for locally stationary regressors and a regression function that changes smoothly over time. These models are a natural extension of time series models with time-varying coefficients. We introduce a kernel-based method to estimate the time-varying regression function and provide asymptotic theory for our estimates. Moreover, we show that the main conditions of the theory are satisfied for a large class of nonlinear autoregressive processes ...
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作者：Candes, Emmanuel J.; Soltanolkotabi, Mahdi
作者单位：Stanford University
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作者：Chandrasekaran, Venkat; Parrilo, Pablo A.; Willsky, Alan S.
作者单位：California Institute of Technology; Massachusetts Institute of Technology (MIT)
摘要：Suppose we observe samples of a subset of a collection of random variables. No additional information is provided about the number of latent variables, nor of the relationship between the latent and observed variables. Is it possible to discover the number of latent components, and to learn a statistical model over the entire collection of variables? We address this question in the setting in which the latent and observed variables are jointly Gaussian, with the conditional statistics of the o...
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作者：Portnoy, Stephen
作者单位：University of Illinois System; University of Illinois Urbana-Champaign
摘要：Traditionally, assessing the accuracy of inference based on regression quantiles has relied on the Bahadur representation. This provides an error of order n(-1/4) in normal approximations, and suggests that inference based on regression quantiles may not be as reliable as that based on other (smoother) approaches, whose errors are generally of order n(-1/2) (or better in special symmetric cases). Fortunately, extensive simulations and empirical applications show that inference for regression q...
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作者：Rivoirard, Vincent; Rousseau, Judith
作者单位：Universite PSL; Universite Paris-Dauphine; Institut Polytechnique de Paris; ENSAE Paris
摘要：In this paper, we study the asymptotic posterior distribution of linear functionals of the density by deriving general conditions to obtain a semiparametric version of the Bernstein-von Mises theorem. The special case of the cumulative distributive function, evaluated at a specific point, is widely considered. In particular, we show that for infinite-dimensional exponential families, under quite general assumptions, the asymptotic posterior distribution of the functional can be either Gaussian...
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作者：Bunea, Florentina; She, Yiyuan; Wegkamp, Marten H.
作者单位：Cornell University; State University System of Florida; Florida State University; Cornell University
摘要：We propose dimension reduction methods for sparse, high-dimensional multivariate response regression models. Both the number of responses and that of the predictors may exceed the sample size. Sometimes viewed as complementary, predictor selection and rank reduction are the most popular strategies for obtaining lower-dimensional approximations of the parameter matrix in such models. We show in this article that important gains in prediction accuracy can be obtained by considering them jointly....
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作者：Castillo, Ismael; van der Vaart, Aad
作者单位：Centre National de la Recherche Scientifique (CNRS); Sorbonne Universite; Universite Paris Cite; Sorbonne Universite; Sorbonne Universite; Universite Paris Cite; Centre National de la Recherche Scientifique (CNRS); Universite Paris Cite; Vrije Universiteit Amsterdam
摘要：We consider full Bayesian inference in the multivariate normal mean model in the situation that the mean vector is sparse. The prior distribution on the vector of means is constructed hierarchically by first choosing a collection of nonzero means and next a prior on the nonzero values. We consider the posterior distribution in the frequentist set-up that the observations are generated according to a fixed mean vector, and are interested in the posterior distribution of the number of nonzero co...
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作者：Bayarri, M. J.; Berger, J. O.; Forte, A.; Garcia-Donato, G.
作者单位：University of Valencia; Duke University; Universitat Jaume I; Universidad de Castilla-La Mancha
摘要：In objective Bayesian model selection, no single criterion has emerged as dominant in defining objective prior distributions. Indeed, many criteria have been separately proposed and utilized to propose differing prior choices. We first formalize the most general and compelling of the various criteria that have been suggested, together with a new criterion. We then illustrate the potential of these criteria in determining objective model selection priors by considering their application to the ...
