Penalized principal logistic regression for sparse sufficient dimension reduction

DOI10.1016/J.CSDA.2016.12.003zbMath1464.62159OpenAlexW2567488862MaRDI QIDQ1654232

Seung Jun Shin, Andreas Artemiou

Publication date: 7 August 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: http://orca.cf.ac.uk/96679/1/manuscript_R1_AA.pdf

zbMATH Keywords

sufficient dimension reductionMax-SCAD penaltyprincipal logistic regressionsparse sufficient dimension reduction

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Generalized linear models (logistic models) (62J12)

Related Items (7)

A brief review of linear sufficient dimension reduction through optimization ⋮ High-dimensional sufficient dimension reduction through principal projections ⋮ Entropy-based test for generalised Gaussian distributions ⋮ Principal weighted logistic regression for sufficient dimension reduction in binary classification ⋮ Graph informed sliced inverse regression ⋮ Central quantile subspace ⋮ On sufficient dimension reduction via principal asymmetric least squares

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