New inference procedures for semiparametric varying-coefficient partially linear cox models

In biomedical research,one major objective is to identify risk factors and study their risk impacts,as this identification can help clinicians to both properly make a decision and increase efficiency of treatments and resource allocation. a two-step penalized-based procedure is proposed to select linear regression coefficients for linear components and to identify significant nonparametric varying-coefficient functions for semiparametric varying-coefficient partially linear cox models. it is shown that the penalized-based resulting estimators of the linear regression coefficients are asymptotically normal and have oracle properties,and the resulting estimators of the varying-coefficient functions have optimal convergence rates. a simulation study and an empirical example are presented for illustration. © 2014 yunbei ma and xuan luo.