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

Yuao Hu and Heng Lian

Statistics & Probability Letters, 2013, vol. 83, issue 1, 61-69

Abstract: 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.

Keywords: Akaike information criterion (AIC); Bayesian information criterion (BIC); Cross-validation; Partial likelihood; SCAD (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (5)

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DOI: 10.1016/j.spl.2012.08.024

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