Penalized principal logistic regression for sparse sufficient dimension reduction

Shin, Seung Jun and Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 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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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 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:	28 Nov 2024 17:45
URI:	https://orca.cardiff.ac.uk/id/eprint/96679

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