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.

中文翻译：

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具有区间删失失效时间数据的广义优势率混合固化模型的变量选择

摘要 许多作者讨论了具有固化分数的失效时间数据的变量选择，但大多数现有方法仅适用于右删失失效时间数据。在本文中，我们考虑了当一个人面临来自一类广义优势率混合治愈模型的区间删失故障时间数据时的变量选择，并且我们通过最大化导出的惩罚似然函数提出了一种惩罚变量选择方法。在该方法中，采用筛法逼近未知函数，并使用新颖的惩罚期望最大化（EM）算法实现。还获得了所提出的回归参数估计量的渐近特性，包括预言机特性。此外，进行了模拟研究以评估所提出方法的有限样本性能，结果表明它在实践中运行良好。最后，该方法应用于一组取自尼日利亚人口与健康调查的儿童死亡率真实数据。