北航经管学院“经济统计论坛”系列讲座
（2026年第7期，总第44期）
讲座题目：Accelerated Failure Time Models for Complex Censored Survival Data
讲座嘉宾：Aishwarya Bhaskaran博士
讲座时间：2026年9月10日（周四），10:40-11:20
讲座地点：新主楼 A836
讲座嘉宾
Aishwarya Bhaskaran is a Lecturer in Statistics in the School of Mathematics and Statistics at the University of New South Wales. She received her PhD in Statistics from the University of Technology Sydney and subsequently completed a postdoctoral fellowship in Statistics at Macquarie University. Her research primarily focuses on likelihood-based inference, with particular emphasis on generalised linear mixed models, semi-parametric methods for survival analysis, penalised methods and asymptotic theory. She has also worked on Bayesian inference using variational approximation methods, with a particular focus on improving variational inference for complex models and applying these methods to large datasets.
讲座概要
Accelerated failure time (AFT) models provide a useful framework for analysing time-to-event data, where the outcome of interest is the time until a particular event occurs. A key advantage of AFT models is their intuitive interpretation: covariates act directly to accelerate or decelerate the time until an event occurs.
While existing methods are well developed for time-fixed covariates and precisely observed event times, many real-world datasets involve covariates that change over time and event times that are only known to lie within an interval. In this talk, we introduce a maximum penalized likelihood approach for fitting a semiparametric AFT model that accommodates both time-fixed and time-varying covariates with partly interval-censored event times. The nonparametric baseline hazard is smoothly approximated within this framework, and the model parameters are estimated using constrained optimization.
We evaluate the proposed approach through simulations and illustrate its application using a randomized clinical trial on advanced melanoma. Although our illustration is clinical, the methodology is broadly applicable to business and economic settings, including customer churn, employee turnover, loan default, and the duration of business relationships.
