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February 2020 Penalized generalized empirical likelihood with a diverging number of general estimating equations for censored data
Niansheng Tang, Xiaodong Yan, Xingqiu Zhao
Ann. Statist. 48(1): 607-627 (February 2020). DOI: 10.1214/19-AOS1870


This article considers simultaneous variable selection and parameter estimation as well as hypothesis testing in censored survival models where a parametric likelihood is not available. For the problem, we utilize certain growing dimensional general estimating equations and propose a penalized generalized empirical likelihood, where the general estimating equations are constructed based on the semiparametric efficiency bound of estimation with given moment conditions. The proposed penalized generalized empirical likelihood estimators enjoy the oracle properties, and the estimator of any fixed dimensional vector of nonzero parameters achieves the semiparametric efficiency bound asymptotically. Furthermore, we show that the penalized generalized empirical likelihood ratio test statistic has an asymptotic central chi-square distribution. The conditions of local and restricted global optimality of weighted penalized generalized empirical likelihood estimators are also discussed. We present a two-layer iterative algorithm for efficient implementation, and investigate its convergence property. The performance of the proposed methods is demonstrated by extensive simulation studies, and a real data example is provided for illustration.


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Niansheng Tang. Xiaodong Yan. Xingqiu Zhao. "Penalized generalized empirical likelihood with a diverging number of general estimating equations for censored data." Ann. Statist. 48 (1) 607 - 627, February 2020.


Received: 1 July 2018; Revised: 1 February 2019; Published: February 2020
First available in Project Euclid: 17 February 2020

zbMATH: 07196553
MathSciNet: MR4065176
Digital Object Identifier: 10.1214/19-AOS1870

Primary: 62N02, 62N03
Secondary: 62F05, 62F12

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.48 • No. 1 • February 2020
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