This paper discusses regression analysis of case K interval-censored failure time data, a general type of failure time data, with informative censoring with the focus on simultaneous variable selection and estimation. Although many authors have considered this challenging problem, most of the existing methods assume independent or non-informative censoring. More importantly, they are frailty model-based approaches and cannot directly assess the degree of informative censoring among other shortcomings. To address these, we propose a conditional approach and develop a penalized sieve maximum likelihood procedure for the simultaneous variable selection and estimation of covariate effects. Furthermore, we establish the oracle property of the proposed method and illustrate the appropriateness and usefulness of the approach using a simulation study. Finally we apply the proposed method to a set of real data on Alzheimer's disease and provide some new insights.

报告人简介：吉林大学数学学院硕士生导师。主持国家自然科学青年基金项目一项。在Statistica Sinica, Statistical Methods in Medical Research, Statistics in Medicine，Computational Statistics & Data Analysis等期刊发表19篇学术论文。目前担任Journal of Applied Statistics 副主编。2023年获得国家天元数学东北中心优秀青年学者奖，2024年获得Institute of Mathematical Statistics (IMS) Junior researcher travel award 和 Elected member of ISI (International Statistical Institute).