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2014 New Inference Procedures for Semiparametric Varying-Coefficient Partially Linear Cox Models
Yunbei Ma, Xuan Luo
J. Appl. Math. 2014: 1-16 (2014). DOI: 10.1155/2014/360249

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.

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Yunbei Ma. Xuan Luo. "New Inference Procedures for Semiparametric Varying-Coefficient Partially Linear Cox Models." J. Appl. Math. 2014 1 - 16, 2014. https://doi.org/10.1155/2014/360249

Information

Published: 2014
First available in Project Euclid: 2 March 2015

zbMATH: 07131549
Digital Object Identifier: 10.1155/2014/360249

Rights: Copyright © 2014 Hindawi

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