2008

最新文章

• GIBBS POSTERIOR FOR VARIABLE SELECTION IN HIGH-DIMENSIONAL CLASSIFICATION AND DATA MINING AI翻译
  作者：Jiang, Wenxin; Tanner, Martin A.
  作者单位：Northwestern University
  摘要：In the popular approach of Bayesian variable selection (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction will be considered here to study BVS with I Gibbs posterior originating in statistical mechanics. The Gibbs posterior is constructed from a risk function of practical interest (Such as the classification error) and aims at minimizing a risk function without modeling the data probabilistically. This can ...
• Admissible predictive density estimation AI翻译
  作者：Brown, Lawrence D.; George, Edward I.; Xu, Xinyi
  作者单位：University of Pennsylvania; University System of Ohio; Ohio State University
  摘要：Let X vertical bar mu similar to N-p (mu, upsilon I-x) and Y vertical bar mu similar to N-p (mu, upsilon I-y) be independent p-dimensional multivariate normal vectors with common unknown mean A. Based on observing X = x, we consider the problem of estimating the true predictive density p(y vertical bar mu) of Y under expected Kullback-Leibler loss. Our focus here is the characterization of admissible procedures for this problem. We show that the class of all generalized Bayes rules is a comple...
• Nonlinear estimation for linear inverse problems with error in the operator AI翻译
  作者：Hoffmann, Marc; Reiss, Markus
  作者单位：Universite Paris-Est-Creteil-Val-de-Marne (UPEC); Centre National de la Recherche Scientifique (CNRS); CNRS - National Institute for Mathematical Sciences (INSMI); Universite Gustave-Eiffel; Ruprecht Karls University Heidelberg
  摘要：We study two nonlinear methods for statistical linear inverse problems when the operator is not known. The two constructions combine Galerkin regularization and wavelet thresholding. Their performances depend on the underlying structure of the operator, quantified by an index of sparsity. We prove their rate-optimality and adaptivity properties over Besov classes.
• A complementary design theory for doubling AI翻译
  作者：Xu, Hongquan; Cheng, Ching-Shui
  作者单位：University of California System; University of California Los Angeles; University of California System; University of California Berkeley
  摘要：Chen and Cheng [Ann. Statist. 34 (2006) 546-558] discussed the method of doubling for constructing two-level fractional factorial designs. They showed that for 9N/32 <= n <= 5N/16, all minimum aberration designs with N runs and n factors are projections of the maximal design with 5N/16 factors which is constructed by repeatedly doubling the 2(5-1) design defined by I = ABCDE. This paper develops a general complementary design theory for doubling. For any design obtained by repeated doubling, g...
• ESTIMATION OF DISTRIBUTIONS, MOMENTS AND QUANTILES IN DECONVOLUTION PROBLEMS AI翻译
  作者：Hall, Peter; Lahiri, Soumendra N.
  作者单位：University of Melbourne; Texas A&M University System; Texas A&M University College Station
  摘要：When using the bootstrap in the presence of measurement error, we must first estimate the target distribution function; we cannot directly resample since we don not have a sample from the target. These and other considerations motivate the development of estimators of distributions, and of related quantities such as moments and quantiles, in errors-in-variables settings. We show that such estimators have curious and unexpected properties. For example, if the distributions of the variable of in...
• General frequentist properties of the posterior profile distribution AI翻译
  作者：Cheng, Guang; Kosorok, Michael R.
  作者单位：Duke University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：In this paper, inference for the parametric component of a semiparametric model based on sampling from the posterior profile distribution is thoroughly investigated from the frequentist viewpoint. The higher-order validity of the profile sampler obtained in Cheng and Kosorok [Ann. Statist. 36 (2008)] is extended to semiparametric models in which the infinite dimensional nuisance parameter may not have a root-n convergence rate. This is a nontrivial extension because it requires a delicate anal...
• Confidence bands in nonparametric time series regression AI翻译
  作者：Zhao, Zhibiao; Wul, Wei Biao
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; University of Chicago
  摘要：We consider nonparametric estimation of mean regression and conditional variance (or volatility) functions in nonlinear stochastic regression models. Simultaneous confidence bands are constructed and the coverage probabilities are shown to be asymptotically correct. The imposed dependence structure allows applications in many linear and nonlinear autoregressive processes. The results are applied to the S&P 500 Index data.
• Optimal rank-based tests for homogeneity of scatter AI翻译
  作者：Hallin, Marc; Paindaveine, Davy
  作者单位：Universite Libre de Bruxelles; Universite Libre de Bruxelles
  摘要：We propose a class of locally and asymptotically optimal tests, based on multivariate ranks and signs for the homogeneity of scatter matrices in M. elliptical populations. Contrary to the existing parametric procedures, these tests remain valid without any moment assumptions, and thus are perfectly robust against heavy-tailed distributions (validity robustness). Nevertheless, they reach semiparametric efficiency bounds at correctly specified elliptical densities and maintain high powers under ...
• Asymptotic properties of bridge estimators in sparse high-dimensional regression models AI翻译
  作者：Huang, Jian; Horowitz, Joel L.; Ma, Shuangge
  作者单位：University of Iowa; Northwestern University; Yale University
  摘要：We Study the asymptotic properties of bridge estimators in sparse, high-dimensional, linear regression models when the number of covariates may increase to infinity with the sample size. We are particularly interested in the use of bridge estimators to distinguish between covariates whose coefficients are zero and covariates whose coefficients are nonzero. We show that under appropriate conditions, bridge estimators correctly select covariates with nonzero coefficients with probability converg...
• Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models AI翻译
  作者：Chatterjee, Snigdhansu; Lahiri, Partha; Li, Huilin
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University System of Maryland; University of Maryland College Park
  摘要：Empirical best linear unbiased prediction (EBLUP) method uses a linear mixed model in combining information from different sources of information. This method is particularly useful in small area problems. The variability of an EBLUP is traditionally measured by the mean squared prediction error (MSPE), and interval estimates are generally constructed using estimates of the MSPE. Such methods have shortcomings like under-coverage or over-coverage, excessive length and lack of interpretability....