Projective resampling estimation of informative predictor subspace for multivariate regression
- 주제(키워드) Clustering mean method , Fused estimation , Informative predictor subspace , K-means clustering , Sufficient dimension reduction
- 등재 SCIE, SCOPUS, KCI등재
- 발행기관 Springer
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000203147
- 본문언어 영어
- Published As https://doi.org/10.1007/s42952-022-00178-0
초록/요약
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. © 2022, Korean Statistical Society.
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