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Penalized principal logistic regression for sparse sufficient dimension reduction

Shin, Seung Jun and Artemiou, Andreas 2017. Penalized principal logistic regression for sparse sufficient dimension reduction. Computational Statistics & Data Analysis 111 , pp. 48-58. 10.1016/j.csda.2016.12.003

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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 extend it to a 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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: Max-SCAD penalty; Principal logistic regression; Sparse sufficient dimension reduction; Sufficient dimension reduction
Publisher: Elsevier
ISSN: 0167-9473
Date of First Compliant Deposit: 9 December 2016
Date of Acceptance: 5 December 2016
Last Modified: 29 Dec 2017 08:53

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