Bagging versions of sliced inverse regression

Vanessa Kuentz, Benoît Liquet, Jérôme Saracco*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Sliced Inverse Regression (SIR) introduced by Li (1991) is a well-known dimension reduction method in semiparametric regression. In this article, we propose bagging versions of SIR which consist in using bootstrap replications of the data set and in aggregating the corresponding estimators. We give the asymptotic distribution of the Bagging-SIR estimator. A simulation study is used to compare the numerical performance of the proposed alternative bagging versions of SIR with the classical SIR approach. The benefits of these methods are significant for noisy models and when the sample size is small. The R codes are available from the authors.

Original languageEnglish
Pages (from-to)1985-1996
Number of pages12
JournalCommunications in Statistics - Theory and Methods
Volume39
Issue number11
DOIs
Publication statusPublished - 2010
Externally publishedYes

Keywords

  • Bootstrap
  • Bagging
  • Effective dimension reduction (edr) space
  • Sliced inverse regression (SIR)

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