Variable selection in a partially linear proportional hazards model with a diverging dimensionality

We consider the problem of simultaneous variable selection and estimation in partially linear proportional hazards models when the number of covariates in the linear part diverges with the sample size. We apply the smoothly clipped absolute deviation (SCAD) penalty to select the significant covariates in the linear part. Some simulations and a real data set are presented.

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Year of publication:	2013
Authors:	Hu, Yuao ; Lian, Heng
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 1, p. 61-69
Publisher:	Elsevier
Subject:	Akaike information criterion (AIC) \| Bayesian information criterion (BIC) \| Cross-validation \| Partial likelihood \| SCAD

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010593895