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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

11 Scopus Citations
View graph of relations



Original languageEnglish
Pages (from-to)61-69
Journal / PublicationStatistics and Probability Letters
Issue number1
Publication statusPublished - Jan 2013
Externally publishedYes


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. © 2012 Elsevier B.V.

Research Area(s)

  • Akaike information criterion (AIC), Bayesian information criterion (BIC), Cross-validation, Partial likelihood, SCAD