Projective resampling estimation of informative predictor subspace for multivariate regression

Sojin Ko, Jae Keun Yoo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

In this paper, a paradigm to estimate the so-called informative predictor subspace (Yoo in Statistics 50:1086–1099, 2016) for multivariate regression is proposed. For this, as a primary target subspace, a projective resampling informative predictor subspace is newly developed. The projective resampling informative predictor subspace is constructed based on a projection resampling method by Li et al. (2008), and it has advantage that it is smaller than the original informative predictor subspace but contains the central subspace. To estimate the new target subspace, the three approaches of projective resampling, coordinate, and coordinate-projective resampling mean methods are proposed. The three methods are investigated via various numerical studies, which confirm their potential usefulness in practice.

Original languageEnglish
Pages (from-to)1117-1131
Number of pages15
JournalJournal of the Korean Statistical Society
Volume51
Issue number4
DOIs
StatePublished - Dec 2022

Bibliographical note

Funding Information:
For Jae Keun Yoo, this work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education (NRF-2021R1F1A1059844). The authors are grateful to the two referees and the Associate Editor for their many helpful comments and suggestions.

Publisher Copyright:
© 2022, Korean Statistical Society.

Keywords

  • Clustering mean method
  • Fused estimation
  • Informative predictor subspace
  • K-means clustering
  • Sufficient dimension reduction

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