報告題目：Factor-Augmented Transformation Models for Interval-Censored Failure Time Data
報 告 人：李樹威 副教授 廣州大學
報告時間：2023年6月20日 13：30-14：30
報告地點：騰訊會議 ID：650387243
校内聯系人：趙世舜 zhaoss@jlu.edu.cn
報告摘要：Interval-censored failure time data frequently arise in various scientific studies where each subject experiences periodical examinations for the occurrence of the failure event of interest, and the failure time is only known to lie in a specific time interval. In addition, collected data may include multiple observed variables with a certain degree of correlation, leading to severe multicollinearity issues. This study proposes a factor-augmented transformation model to analyze interval-censored failure time data while reducing model dimensionality and avoiding multicollinearity elicited by multiple correlated covariates.We provide a joint modeling framework by comprising a factor analysis model to group multiple observed variables into a few latent factors and a class of semiparametric transformation models with the augmented factors to examine their and other covariate effects on the failure event.Furthermore, we propose a nonparametric maximum likelihood estimation approach and develop a computationally stable and reliable expectation-maximization algorithm for its implementation.We establish the asymptotic properties of the proposed estimators and conduct simulation studies to assess the empirical performance of the proposed method. An application to the Alzheimer's Disease Neuroimaging Initiative study is provided. An R package ICTransCFA is also available for practitioners.
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。
