报告题目：Variable selection for generalized odds rate mixture cure models with interval-censored failure time data
报告时间：2023年11月13日10：00-13：00
报告地点：理学院 405
报 告 人：赵世舜 教授
报告人简介：
赵世舜，教授，吉林大学数学学院，吉林大学获得博士学位，师从于史宁中教授。于2013年-2014年在美国密苏里大学做访问学者，近年来一直从事生存分析、多元统计以及大数据方向的研究。在国内外名杂志已发表论文SCI论文20余篇，包括区间删失的研究、相依区间删失的研究以及特征选择方向的研究。作为项目负责人主持国家自然科学面上项目2项，教育部科研项目1项，省自然科学基金2项。作为主要参加人参加国家自然科学基金项目3项。
报告内容：
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.
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