Variable selection for generalized odds rate mixture cure models with interval-censored failure time data

Yang Xu, Shishun Zhao, Tao Hu and Jianguo Sun

Computational Statistics & Data Analysis, 2021, vol. 156, issue C

Abstract: Variable selection for failure time data with a cured fraction has been discussed by many authors but most of existing methods apply only to right-censored failure time data. In this paper, we consider variable selection when one faces interval-censored failure time data arising from a general class of generalized odds rate mixture cure models, and we propose a penalized variable selection method by maximizing a derived penalized likelihood function. In the method, the sieve approach is employed to approximate the unknown function, and it is implemented using a novel penalized expectation–maximization (EM) algorithm. Also the asymptotic properties of the proposed estimators of regression parameters, including the oracle property, are obtained. Furthermore, a simulation study is conducted to assess the finite sample performance of the proposed method, and the results indicate that it works well in practice. Finally, the approach is applied to a set of real data on childhood mortality taken from the Nigeria Demographic and Health Survey.

Keywords: EM algorithm; Generalized odds rate mixture cure model; Penalized maximum likelihood estimators; Sieve approach (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:156:y:2021:i:c:s0167947320302061

DOI: 10.1016/j.csda.2020.107115

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