讲座人简介：赵世舜，吉林大学数学学院教授、博士生导师，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。在国内外知名杂志发表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。

讲座简介：Variable selection is often needed in many fields and has been discussed by manyauthors 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 squared method with the use of an unbiased transformation (Deng et al. 2012), and the oracle property of the method is established along with the asymptotic normality of the resulting estimators of regression parameters. Simulation studies are conducted and demonstrated that the proposed method performs well for practical situations. In addition, the method is applied to a motivating example about children's mortality data of Nigeria.