Projective resampling estimation of informative predictor subspace for multivariate regression

Sojin Ko, Jae Keun Yoo

Research output: Contribution to journalArticlepeer-review

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
JournalJournal of the Korean Statistical Society
DOIs
StateAccepted/In press - 2022

Keywords

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

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