報告題目：Semiparametric Probit Regression Model with General Interval-Censored Failure Time Data
報 告 人： 李樹威 副教授 廣州大學
報告時間：2022年9月22日 星期四10:30-11:30
報告地點：騰訊會議 297491236
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：Interval-censored data frequently arise in various biomedical areas involving periodical follow-ups where the failure or event time of interest cannot be observed exactly but is only known to fall into a time interval. This article considers a semiparametric probit regression model, a valuable alternative to other commonly used semiparametric models in survival analysis, to investigate potential risk factors for the interval-censored failure time of interest. We develop an expectation-maximization (EM) algorithm to conduct the nonparametric maximum likelihood estimation (NPMLE) using the working independence strategy for general or mixed-case interval-censored data. The resulting estimators of regression parameters are shown to be consistent, asymptotically normal, and semi-parametrically efficient. In addition, we propose a novel penalized EM algorithm for simultaneously achieving variable selection and parameter estimation in the case of high-dimensional covariates. The proposed variable selection method can be readily implemented with some existing software and considerably reduces the estimation error of the proposed NPMLE approach. Simulation studies demonstrate the satisfactory performance of the proposed methods. An application to a set of interval-censored data on prostate cancer further confirms the utility of the methodology.
報告人簡介：李樹威，廣州大學統計系副教授、研究生導師。研究領域為生物統計、生存分析、縱向數據等。擔任多個學會的常務理事和理事，主持國家自然科學基金青年基金等項目，發表多篇SCI論文。
