报告题目：Bayesian variable selection in a binary quantile regression model for longitudinal data
报 告 人：李纯净 副教授
所在单位：长春工业大学
报告时间：2024年12月10日 星期二 14:15-15:00
报告地点：吉林大学中心校区数学楼第二报告厅
校内联系人：程建华 chengjh@jlu.edu.cn
报告摘要：Double-censored data arise when the failure time of interest can be observed only if it is within an interval or a window. Such data can occur in many areas and many methods have been proposed for their analyses. However, most of the existing methods assume that or apply only to the situation where the censoring is independent. On the other hand, it is well-known this may not be true or one may face informative censoring in many cases. To address this, we present a generalized accelerated hazards frailty model that allows for dependent censoring among other advantages and includes the proportional hazards, accelerated failure time and traditional accelerated hazards models as special cases. For inference, we propose a joint model-based sieve maximum likelihood approach and develop an EM-based algorithm for its implementation, Also the profile approach is adapted for variance estimation and the proposed estimator of regression parameters is shown to be consistent and asymptotically normal. Furthermore, an extensive simulation study is performed and suggests that the proposed method works well in practical situations and it is applied to a set of real data from an AIDS clinical trial that motivated this study.
报告人简介：李纯净，博士生导师，任长春工业大学数学与统计学院副院长。现任吉林省工业与应用数学学会常务理事，副秘书长，全国工业统计学教学研究会第九届理事会理事。目前主要从事生存分析、贝叶斯经验似然的研究。主持国家社会科学基金一般项目、国家统计局全国统计科研计划重点项目、吉林省统计局重大项目各1项、吉林省智库项目1项、省教育厅“十三五”科学研究规划项目1项、主参4项国家自然科学基金等课题。在国内外知名学术期刊发表20余篇高水平学术论文。
