期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 111, 期 -, 页码 48-58出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2016.12.003
关键词
Max-SCAD penalty; Principal logistic regression; Sparse sufficient dimension reduction; Sufficient dimension reduction
资金
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [2015R1C1A1A01054913]
- National Research Foundation of Korea [2015R1C1A1A01054913] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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