报告题目：Variable selection for generalized linear models with interval-censored failure time data

报告时间：2022年6月28 日 下午 15:00

会议链接：https://meeting.tencent.com/dm/W0Up9BUuh0ps

会议 ID：959-847-580

主办单位：科研处/数学与统计学院

主讲人：胡涛

胡涛简介：首都师范大学数学科学学院教授，博士生导师。研究方向：生存分析、风能数据分析。2009年毕业于北京师范大学数学科学学院，获概率论与数理统计专业博士学位。美国University of Missouri 统计系博士后。在国内外学术刊物Journal of the American Statistical Association、Biometrika、Renewable Energy、Energy Conversion and Management、中国科学：数学等上发表学术论文多篇。

摘要：Variable selection is often needed in many fields and has been discussed by many authors in various situations. This is especially the case under linear models and when one observes complete data. Among others, one common situation where variable selection is required is to identify important risk factors from a large number of covariates. In this paper, we consider the problem when one observes interval-censored failure time data arising from generalized linear models, for which there does not seem to exist an established method. To address this, we propose a penalized least squares method with the use of an unbiased transformation and the oracle property of the method is established along with the asymptotic normality of the resulting estimators of regression parameters. Simulation studies were conducted and demonstrated that the proposed method performed well for practical situations. In addition, the method was applied to a motivating example about children’s mortality data of Nigeria.