Online Meeting Link

Abstract: 
Survival analysis is a branch of statistics that studies time-to-event data or survival data. The main feature of survival data is that the response variable is only partially observed and subject to censoring and/or truncation caused by the nature of study design. In this talk, I will briefly discuss different types of survival data and existing popular semiparametric survival models in the literature. Then I will discuss my recent work in detail on regression analysis of arbitrarily censored data and left truncated data. The proposed estimation approaches are developed based on EM algorithms and enjoy several nice properties such as being easy to implement, robust to initial values, fast to converge, and providing variance estimates in closed form.

Speaker's Bio:
Dr. Lianming Wang is an Associate Professor in the Department of Statistics at University of South Carolina. His research areas include survival analysis,  longitudinal data analysis, categorical data analysis, multivariate analysis, statistical computing, nonparametric and semiparametric modeling, and biomedical applications. His research goal is to develop sound statistical approaches for analyzing complex data with various structures in real life studies of all fields.