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作者：Cook, RD; Bing, L
作者单位：University of Minnesota System; University of Minnesota Twin Cities; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
摘要：The central mean subspace (CMS) and iterative Hessian transformation (IHT) have been introduced recently for dimension reduction when the conditional mean is of interest. Suppose that X is a vector-valued predictor and Y is a scalar response. The basic problem is to find a lower-dimensional predictor n(T)X such that E(Y\X) = E(Y\n(T)X). The CMS defines the inferential object for this problem and IHT provides an estimating procedure. Compared with other methods, IHT requires fewer assumptions a...
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作者：Beran, R
作者单位：University of California System; University of California Davis
摘要：This paper constructs improved estimators of the means in the Gaussian saturated one-way layout with an ordinal factor. The least squares estimator for the mean vector in this saturated model is usually inadmissible. The hybrid shrinkage estimators of this paper exploit the possibility of slow variation in the dependence of the means on the ordered factor levels but do not assume it and respond well to faster variation if present. To motivate the development, candidate penalized least squares ...
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作者：Horowitz, JL; Mammen, E
作者单位：Northwestern University; University of Mannheim
摘要：This paper describes an estimator of the additive components of a nonparametric additive model with a known link function. When the additive components are twice continuously differentiable, the estimator is asymptotically normally distributed with a rate of convergence in probability of n(-2/5). This is true regardless of the (finite) dimension of the explanatory variable. Thus, in contrast to the existing asymptotically normal estimator, the new estimator has no curse of dimensionality. More...
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作者：Jing, BY; Shao, QM; Zhou, W
作者单位：Hong Kong University of Science & Technology; University of Oregon; National University of Singapore
摘要：A saddlepoint approximation of the Student's t-statistic was derived by Daniels and Young [Biometrika 78 (1991) 169-179] under the very stringent exponential moment condition that requires that the underlying density function go down at least as fast as a Normal density in the tails. This is a severe restriction on the approximation's applicability. In this paper we show that this strong exponential moment restriction can be completely dispensed with, that is, saddlepoint approximation of the ...
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作者：Kou, SC
作者单位：Harvard University
摘要：This paper studies, under the setting of spline regression, the connection between finite-sample properties of selection criteria and their asymptotic counterparts, focusing on bridging the gap between the two. We introduce a bias-variance decomposition of the prediction error, using which it is shown that in the asymptotics the bias term dominates the variability term, providing an explanation of the gap. A geometric exposition is provided for intuitive understanding. The theoretical and geom...
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作者：Hallin, M; Paindaveine, D
作者单位：Universite Libre de Bruxelles
摘要：We are deriving optimal rank-based tests for the adequacy of a vector autoregressive-moving average (VARMA) model with elliptically contoured innovation density. These tests are based on the ranks of pseudo-Mahalanobis distances and on normed residuals computed from Tyler's [Ann. Statist. 15 (1987) 234-251] scatter matrix; they generalize the univariate signed rank procedures proposed by Hallin and Puri [J. Multivariate Anal. 39 (1991) 1-29]. Two types of optimality properties are considered, ...
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作者：Chopin, N
作者单位：University of Bristol
摘要：The term sequential Monte Carlo methods or, equivalently, particle filters, refers to a general class of iterative algorithms that performs Monte Carlo approximations of a given sequence of distributions of interest (pi(t)). We establish in this paper a central limit theorem for the Monte Carlo estimates produced by these computational methods. This result holds under minimal assumptions on the distributions pi(t), and applies in a general framework which encompasses most of the sequential Mon...
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作者：Carter, A; Pollard, D
作者单位：University of California System; University of California Santa Barbara; Yale University
摘要：Tusnady's inequality is the key ingredient in the KMT/Hungarian coupling of the empirical distribution function with a Brownian bridge. We present an elementary proof of a result that sharpens the Tusnady inequality, modulo constants. Our method uses the beta integral representation of Binomial tails, simple Taylor expansion and some novel bounds for the ratios of normal tail probabilities.
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作者：Johnson, VE
作者单位：University of Michigan System; University of Michigan
摘要：This article describes an extension of classical chi(2) goodness-of-fit tests to Bayesian model assessment. The extension, which essentially involves evaluating Pearson's goodness-of-fit statistic at a parameter value drawn from its posterior distribution, has the important property that it is asymptotically distributed as a chi(2) random variable on K - 1 degrees of freedom, independently of the dimension of the underlying parameter vector. By examining the posterior distribution of this stat...
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作者：Aerts, M; Claeskens, G; Hart, JD
作者单位：Hasselt University; KU Leuven; Texas A&M University System; Texas A&M University College Station
摘要：We propose and analyze nonparametric tests of the null hypothesis that a function belongs to a specified parametric family. The tests are based on BIC approximations, pi(BIC), to the posterior probability of the null model, and may be carried out in either Bayesian or frequentist fashion. We obtain results on the asymptotic distribution Of pi(BIC) under both the null hypothesis and local alternatives. One version Of pi(BIC), call it pi*(BIC), uses a class of models that are orthogonal to each ...
