Sufficient dimension reduction through informative predictor subspace

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5 Scopus citations

Abstract

The purpose of this paper is to define the central informative predictor subspace to contain the central subspace and to develop methods for estimating the former subspace. Potential advantages of the proposed methods are no requirements of linearity, constant variance and coverage conditions in methodological developments. Therefore, the central informative predictor subspace gives us the benefit of restoring the central subspace exhaustively despite failing the conditions. Numerical studies confirm the theories, and real data analyses are presented.

Original languageEnglish
Pages (from-to)1086-1099
Number of pages14
JournalStatistics
Volume50
Issue number5
DOIs
StatePublished - 2 Sep 2016

Keywords

  • central subspace
  • informative predictor subspace
  • linearity condition
  • regression
  • sufficient dimension reduction

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