5期

最新文章

• BAYESIAN DETECTION OF IMAGE BOUNDARIES AI翻译
  作者：Li, Meng; Ghosal, Subhashis
  作者单位：Duke University; North Carolina State University
  摘要：Detecting boundary of an image based on noisy observations is a fundamental problem of image processing and image segmentation. For a d-dimensional image (d = 2, 3,...), the boundary can often be described by a closed smooth (d - 1)-dimensional manifold. In this paper, we propose a nonparametric Bayesian approach based on priors indexed by Sd-1, the unit sphere in R-d. We derive optimal posterior contraction rates for Gaussian processes or finite random series priors using basis functions such...
• GAUSSIAN APPROXIMATION FOR HIGH DIMENSIONAL TIME SERIES AI翻译
  作者：Zhang, Danna; Wu, Wei Biao
  作者单位：University of California System; University of California San Diego; University of Chicago
  摘要：We consider the problem of approximating sums of high dimensional stationary time series by Gaussian vectors, using the framework of functional dependence measure. The validity of the Gaussian approximation depends on the sample size n, the dimension p, the moment condition and the dependence of the underlying processes. We also consider an estimator for long-run covariance matrices and study its convergence properties. Our results allow constructing simultaneous confidence intervals for mean ...
• NONASYMPTOTIC ANALYSIS OF SEMIPARAMETRIC REGRESSION MODELS WITH HIGH-DIMENSIONAL PARAMETRIC COEFFICIENTS AI翻译
  作者：Zhu, Ying
  作者单位：Michigan State University
  摘要：We consider a two-step projection based Lasso procedure for estimating a partially linear regression model where the number of coefficients in the linear component can exceed the sample size and these coefficients belong to the l(q) -balls for q is an element of [0, 1]. Our theoretical results regarding the properties of the estimators are nonasymptotic. In particular, we establish a new nonasymptotic oracle result: Although the error of the nonparametric projection per se (with respect to the...
• SPARSE CCA: ADAPTIVE ESTIMATION AND COMPUTATIONAL BARRIERS AI翻译
  作者：Gao, Chao; Ma, Zongming; Zhou, Harrison H.
  作者单位：University of Chicago; University of Pennsylvania; Yale University
  摘要：Canonical correlation analysis is a classical technique for exploring the relationship between two sets of variables. It has important applications in analyzing high dimensional datasets originated from genomics, imaging and other fields. This paper considers adaptive minimax and computationally tractable estimation of leading sparse canonical coefficient vectors in high dimensions. Under a Gaussian canonical pair model, we first establish separate minimax estimation rates for canonical coeffi...
• BAYESIAN POISSON CALCULUS FOR LATENT FEATURE MODELING VIA GENERALIZED INDIAN BUFFET PROCESS PRIORS AI翻译
  作者：James, Lancelot F.
  作者单位：Hong Kong University of Science & Technology
  摘要：Statistical latent feature models, such as latent factor models, are models where each observation is associated with a vector of latent features. A general problem is how to select the number/types of features, and related quantities. In Bayesian statistical machine learning, one seeks (nonparametric) models where one can learn such quantities in the presence of observed data. The Indian Buffet Process (IBP), devised by Griffiths and Ghahramani (2005), generates a (sparse) latent binary matri...
• DETECTION AND FEATURE SELECTION IN SPARSE MIXTURE MODELS AI翻译
  作者：Verzelen, Nicolas; Arias-Castro, Ery
  作者单位：INRAE; University of California System; University of California San Diego
  摘要：We consider Gaussian mixture models in high dimensions, focusing on the twin tasks of detection and feature selection. Under sparsity assumptions on the difference in means, we derive minimax rates for the problems of testing and of variable selection. We find these rates to depend crucially on the knowledge of the covariance matrices and on whether the mixture is symmetric or not. We establish the performance of various procedures, including the top sparse eigenvalue of the sample covariance ...
• CONFOUNDER ADJUSTMENT IN MULTIPLE HYPOTHESIS TESTING AI翻译
  作者：Wang, Jingshu; Zhao, Qingyuan; Hastie, Trevor; Owen, Art B.
  作者单位：University of Pennsylvania; Stanford University
  摘要：We consider large-scale studies in which thousands of significance tests are performed simultaneously. In some of these studies, the multiple testing procedure can be severely biased by latent confounding factors such as batch effects and unmeasured covariates that correlate with both primary variable( s) of interest (e.g., treatment variable, phenotype) and the outcome. Over the past decade, many statistical methods have been proposed to adjust for the confounders in hypothesis testing. We un...
• MINIMAX ESTIMATION OF A FUNCTIONAL ON A STRUCTURED HIGH-DIMENSIONAL MODEL AI翻译
  作者：Robins, James M.; Li, Lingling; Mukherjee, Rajarshi; Tchetgen, Eric Tchetgen; van der Vaart, Aad
  作者单位：Harvard University; Harvard T.H. Chan School of Public Health; Sanofi-Aventis; Genzyme Corporation; Stanford University; Leiden University - Excl LUMC; Leiden University
  摘要：We introduce a new method of estimation of parameters in semiparametric and nonparametric models. The method employs U-statistics that are based on higher-order influence functions of the parameter of interest, which extend ordinary linear influence functions, and represent higher derivatives of this parameter. For parameters for which the representation cannot be perfect the method often leads to a bias-variance trade-off, and results in estimators that converge at a slower thanv root n-rate....
• ASYMPTOTIC THEORY OF GENERALIZED ESTIMATING EQUATIONS BASED ON JACK-KNIFE PSEUDO-OBSERVATIONS AI翻译
  作者：Overgaard, Morten; Parner, Erik Thorlund; Pedersen, Jan
  作者单位：Aarhus University; Aarhus University
  摘要：A general asymptotic theory of estimates from estimating functions based on jack-knife pseudo-observations is established by requiring that the underlying estimator can be expressed as a smooth functional of the empirical distribution. Using results in p-variation norms, the theory is applied to important estimators from time-to-event analysis, namely the Kaplan-Meier estimator and the Aalen-Johansen estimator in a competing risks model, and the corresponding estimators of restricted mean surv...
• ON THE CONTRACTION PROPERTIES OF SOME HIGH-DIMENSIONAL QUASI-POSTERIOR DISTRIBUTIONS AI翻译
  作者：Atchade, Yves A.
  作者单位：University of Michigan System; University of Michigan
  摘要：We study the contraction properties of a quasi-posterior distribution (sic)(n, d) obtained by combining a quasi-likelihood function and a sparsity inducing prior distribution on R-d, as both n (the sample size), and d (the dimension of the parameter) increase. We derive some general results that highlight a set of sufficient conditions under which (sic)(n, d) puts increasingly high probability on sparse subsets of R-d, and contracts toward the true value of the parameter. We apply these result...