Multivariate seeded dimension reduction
- 주제(키워드) Large p small n , Multivariate regression , Seed matrix , Sufficient dimension reduction
- 등재 SCIE, KCI등재
- 발행기관 KOREAN STATISTICAL SOC
- 발행년도 2014
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000113680
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.jkss.2014.03.002
초록/요약
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. (C) 2014 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
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