Journal of Applied Mathematics

New Inference Procedures for Semiparametric Varying-Coefficient Partially Linear Cox Models

Yunbei Ma and Xuan Luo

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Abstract

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.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 360249, 16 pages.

Dates
First available in Project Euclid: 2 March 2015

Permanent link to this document
https://projecteuclid.org/euclid.jam/1425305788

Digital Object Identifier
doi:10.1155/2014/360249

Citation

Ma, Yunbei; Luo, Xuan. New Inference Procedures for Semiparametric Varying-Coefficient Partially Linear Cox Models. J. Appl. Math. 2014 (2014), Article ID 360249, 16 pages. doi:10.1155/2014/360249. https://projecteuclid.org/euclid.jam/1425305788


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