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Penalized principal logistic regression for sparse sufficient dimension reduction

Authors
Shin, Seung JunArtemiou, Andreas
Issue Date
7월-2017
Publisher
ELSEVIER SCIENCE BV
Keywords
Max-SCAD penalty; Principal logistic regression; Sparse sufficient dimension reduction; Sufficient dimension reduction
Citation
COMPUTATIONAL STATISTICS & DATA ANALYSIS, v.111, pp.48 - 58
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume
111
Start Page
48
End Page
58
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/82938
DOI
10.1016/j.csda.2016.12.003
ISSN
0167-9473
Abstract
Sufficient dimension reduction (SDR) is a successive tool for reducing the dimensionality of predictors by finding the central subspace, a minimal subspace of predictors that preserves all the regression information. When predictor dimension is large, it is often assumed that only a small number of predictors is informative. In this regard, sparse SDR is desired to achieve variable selection and dimension reduction simultaneously. We propose a principal logistic regression (PLR) as a new SDR tool and further develop its penalized version for sparse SDR. Asymptotic analysis shows that the penalized PLR enjoys the oracle property. Numerical investigation supports the advantageous performance of the proposed methods. (C) 2016 Elsevier B.V. All rights reserved.
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