On permutation tests for predictor contribution in sufficient dimension reduction

  • Yuexiao Dong
  • , Chaozheng Yang
  • , Zhou Yu*
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

To test predictor contribution in a model-free fashion, marginal coordinate tests based on sliced inverse regression (SIR) and sliced average variance estimation (SAVE) have been studied in Cook (2004), and Shao et al. (2007) respectively. Estimating the null distributions of the test statistics is a critical step for such tests. We propose a novel permutation test approach to facilitate the marginal coordinate tests, which applies to existing tests based on SIR and SAVE, and can be readily extended to a new marginal coordinate test based on directional regression (Li and Wang, 2007).

Original languageEnglish
Pages (from-to)81-91
Number of pages11
JournalJournal of Multivariate Analysis
Volume149
DOIs
StatePublished - 1 Jul 2016

Keywords

  • Directional regression
  • Nonparametric test
  • Sliced average variance estimation
  • Sliced inverse regression

Fingerprint

Dive into the research topics of 'On permutation tests for predictor contribution in sufficient dimension reduction'. Together they form a unique fingerprint.

Cite this