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Sufficient dimension reduction through informative predictor subspace

Title
Sufficient dimension reduction through informative predictor subspace
Authors
Yoo, Jae Keun
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2016
Journal Title
STATISTICS
ISSN
0233-1888JCR Link1029-4910JCR Link
Citation
vol. 50, no. 5, pp. 1086 - 1099
Keywords
central subspaceinformative predictor subspacelinearity conditionregressionsufficient dimension reduction
Publisher
TAYLOR & FRANCIS LTD
Indexed
SCI; SCIE; SCOPUS WOS scopus
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.
DOI
10.1080/02331888.2016.1148151
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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