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February 2007 Hazard models with varying coefficients for multivariate failure time data
Jianwen Cai, Jianqing Fan, Haibo Zhou, Yong Zhou
Ann. Statist. 35(1): 324-354 (February 2007). DOI: 10.1214/009053606000001145

Abstract

Statistical estimation and inference for marginal hazard models with varying coefficients for multivariate failure time data are important subjects in survival analysis. A local pseudo-partial likelihood procedure is proposed for estimating the unknown coefficient functions. A weighted average estimator is also proposed in an attempt to improve the efficiency of the estimator. The consistency and asymptotic normality of the proposed estimators are established and standard error formulas for the estimated coefficients are derived and empirically tested. To reduce the computational burden of the maximum local pseudo-partial likelihood estimator, a simple and useful one-step estimator is proposed. Statistical properties of the one-step estimator are established and simulation studies are conducted to compare the performance of the one-step estimator to that of the maximum local pseudo-partial likelihood estimator. The results show that the one-step estimator can save computational cost without compromising performance both asymptotically and empirically and that an optimal weighted average estimator is more efficient than the maximum local pseudo-partial likelihood estimator. A data set from the Busselton Population Health Surveys is analyzed to illustrate our proposed methodology.

Citation

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Jianwen Cai. Jianqing Fan. Haibo Zhou. Yong Zhou. "Hazard models with varying coefficients for multivariate failure time data." Ann. Statist. 35 (1) 324 - 354, February 2007. https://doi.org/10.1214/009053606000001145

Information

Published: February 2007
First available in Project Euclid: 6 June 2007

zbMATH: 1114.62104
MathSciNet: MR2332278
Digital Object Identifier: 10.1214/009053606000001145

Subjects:
Primary: 62G05
Secondary: 62N01, 62N02

Rights: Copyright © 2007 Institute of Mathematical Statistics

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Vol.35 • No. 1 • February 2007
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