Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

A Sparse eigen-decomposition estimation for semiparametric models

Language

English

Abstract

For semiparametric models, one of the key issues is to reduce the predictors' dimension so that the regression functions can be efficiently estimated based on the low-dimensional projections of the original predictors. Many sufficient dimension reduction methods seek such principal projections by conducting the eigen-decomposition technique on some method-specific candidate matrices. In this paper, we propose a sparse eigen-decomposition strategy by shrinking small sample eigenvalues to zero. Different from existing methods, the new method can simultaneously estimate basis directions and structural dimension of the central (mean) subspace in a data-driven manner. The oracle property of our estimation procedure is also established. Comprehensive simulations and a real data application are reported to illustrate the efficacy of the new proposed method. © 2009 Elsevier B.V. All rights reserved.

Publication Date

2010

Source Publication Title

Computational Statistics and Data Analysis

Volume

54

Issue

4

Start Page

976

End Page

986

Publisher

Elsevier

DOI

10.1016/j.csda.2009.10.011

Link to Publisher's Edition

http://dx.doi.org/10.1016/j.csda.2009.10.011

ISSN (print)

01679473

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