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Projective resampling estimation of informative predictor subspace for multivariate regression

Title
Projective resampling estimation of informative predictor subspace for multivariate regression
Authors
Ko S.Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2022
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
Journal of the Korean Statistical Society vol. 51, no. 4, pp. 1117 - 1131
Keywords
Clustering mean methodFused estimationInformative predictor subspaceK-means clusteringSufficient dimension reduction
Publisher
Springer
Indexed
SCIE; SCOPUS; KCI scopus
Document Type
Article
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. © 2022, Korean Statistical Society.
DOI
10.1007/s42952-022-00178-0
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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