报告简介  Abstract

Abstract: We propose methods to estimate sufficient reductions of matrix-valued predictors for regression or classification.  We assume that the first moment of the predictor matrix given the response can be decomposed into a row and column component via a Kronecker product structure. We obtain least squares and maximum likelihood estimates of the sufficient reductions of the matrix predictors, derive statistical properties of the resulting estimates and present fast computational algorithms with assured convergence.  The performance of the proposed approaches in regression and classification is compared  in simulations. We illustrate the methods  on two examples, using longitudinally measured serum biomarker and neuroimaging data.

嘉宾简介  About the Speaker

Prof. Bura is currenly in Applied Statistics Research Unit (ASTAT) in the Institute of Statistics and Mathematical Methods in Economics with the Faculty of Mathematics and Geoinformation at the Vienna University of Technology (TU Wien).Her work focuses on dimension reduction in regression and classification, high-dimensional statistics, multivariate analysis, and applications in biostatistics, econometrics and legal statistics.

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