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Bi-level variable selection in semiparametric transformation mixture cure models for right-censored data
Communications in Statistics - Simulation and Computation ( IF 0.8 ) Pub Date : 2021-05-17 , DOI: 10.1080/03610918.2021.1926499
Jingjing Wu 1 , Xuewen Lu 1 , Wenyan Zhong 1, 2
Affiliation  

Abstract

We investigate the bi-level variable selection problem in semiparametric transformation mixture cure models (STMCM). In this type of mixture cure models, we consider a class of semiparametric transformation models for the conditional survival function and a logistic regression for the incidence component, then conduct group variable selection. The group bridge penalty is adopted for bi-level variable selection on both parts of the mixture cure models. Through simulation studies and real data analyses, we show that the proposed method can identify the important variables and groups that contribute to the cure proportion and the survival function for the uncured subjects, respectively.



中文翻译:

右删失数据半参数变换混合治愈模型中的双层变量选择

摘要

我们研究半参数变换混合固化模型(STMCM)中的双层变量选择问题。在这种类型的混合治愈模型中,我们考虑一类条件生存函数的半参数变换模型和发生率分量的逻辑回归,然后进行组变量选择。混合物固化模型的两部分均采用组桥惩罚进行双层变量选择。通过模拟研究和真实数据分析,我们表明所提出的方法可以分别识别对未治愈受试者的治愈比例和生存函数有贡献的重要变量和组。

更新日期:2021-05-17
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