Sparse sufficient dimension reduction using optimal scoring

Tao Wang, Lixing ZHU*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)223-232
Number of pages10
JournalComputational Statistics and Data Analysis
Volume57
Issue number1
DOIs
Publication statusPublished - Jan 2013

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

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

Fingerprint

Dive into the research topics of 'Sparse sufficient dimension reduction using optimal scoring'. Together they form a unique fingerprint.

Cite this