http://dx.doi.org/10.1016/j.csda.2012.06.015">
 

Document Type

Journal Article

Department/Unit

Department of Mathematics

Title

Sparse sufficient dimension reduction using optimal scoring

Language

English

Abstract

Sufficient dimension reduction is a body of theory and methods for reducing the dimensionality of predictors while preserving information on regressions. In this paper we propose a sparse dimension reduction method to perform interpretable dimension reduction. It is designed for situations in which the number of correlated predictors is very large relative to the sample size. The new procedure is based on the optimal scoring interpretation of the sliced inverse regression method. As a result, the regression framework of optimal scoring facilitates the use of commonly used regularization techniques. Simulation studies demonstrate the effectiveness and efficiency of the proposed approach. © 2012 Elsevier B.V. All rights reserved.

Keywords

High dimensionality, Linear discriminant analysis, Optimal scoring, Sliced inverse regression, Sparsity, Sufficient dimension reduction

Publication Date

2013

Source Publication Title

Computational Statistics & Data Analysis

Volume

57

Issue

1

Start Page

223

End Page

232

Publisher

Elsevier

ISSN (print)

01679473

This document is currently not available here.

Share

COinS