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.

Speaker Biography: Mingyue Du

Mingyue Du is a Master's Supervisor at the School of Mathematics, Jilin University. She has presided over one Young Scientists Fund project from the National Natural Science Foundation of China (NSFC).

She has published 19 academic papers in renowned international journals such as Statistica Sinica, Statistical Methods in Medical Research, Statistics in Medicine, and Computational Statistics & Data Analysis. Currently, she serves as an Associate Editor for the Journal of Applied Statistics.

Her outstanding contributions have been recognized with several awards, including the National Tianyuan Mathematics Northeast Center Outstanding Young Scholar Award (2023), the Institute of Mathematical Statistics (IMS) Junior Researcher Travel Award (2024), and she was elected as a member of the International Statistical Institute (ISI).