Multivariate seeded dimension reduction

Jae Keun Yoo, Yunju Im

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A recently introduced seeded dimension reduction approach enables existing sufficient dimension reduction methods to be used in regressions with n<. p. The dimension reduction is accomplished through successive projections of seed matrices on a subspace to contain the central subspace. In the article, we will develop a seeded dimension reduction for multivariate regression, whose responses are multi-dimensional. For this we suggest two conditions that the dimension reduction is attained without the loss of information of the central subspace. Based on this, we construct possible candidate seed matrices. Numerical studies and two data analyses are presented.

Original languageEnglish
Pages (from-to)559-566
Number of pages8
JournalJournal of the Korean Statistical Society
Volume43
Issue number4
DOIs
StatePublished - 1 Dec 2014

Bibliographical note

Funding Information:
For Yunju Im, this work was supported by the BK21 Plus Project through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education ( 22A20130011003 ).

Funding Information:
For Jae Keun Yoo, this works was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korean Ministry of Education ( NRF-2012R1A1A1040077 ).

Publisher Copyright:
© 2014 The Korean Statistical Society.

Keywords

  • Large p small n
  • Multivariate regression
  • Seed matrix
  • Sufficient dimension reduction

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