绿帽社

Li-Pang Chen, PhD

Associate Professor, Dept of Statistics,聽National Chengchi University (NCCU)

Note: Meet & Greet Prof Li-Pang Chen from 3-3:30pm in Room 1140; Prior to seminar 3:30-4:30pm

WHEN: Wednesday, September 17, 2025, from 3:30 to 4:30 p.m.
WHERE: Hybrid | 2001 绿帽社 College Avenue, Rm 1140;
NOTE:聽Li-Pang Chen will be presenting in-person

Abstract

In medical studies and bioinformatics, an important research direction is the analysis of time-to-event data, where the main challenges often arise from incompleteness due to censoring mechanisms. With the growing ease of data collection, it is now common to encounter datasets with a large number of variables. Among these, even rare variables may carry valuable information. Another major challenge is measurement error, a typical feature of noisy data. In my presentation, I will introduce my recent work on survival analysis with multivariate or high-dimensional error-prone variables from the perspective of statistical machine learning. Specifically, I will first present graphical proportional hazards models, which incorporate network structures among variables. To simultaneously handle variable selection and network detection, I propose a penalized likelihood approach with a double-penalty function. Next, I will introduce the accelerated failure time model for interval-censored survival data. To perform variable selection when the estimating functions are possibly non-differentiable, a boosting algorithm is developed to identify informative variables and provide associated estimation. A key advantage of this approach is that it avoids optimization with penalty functions. Finally, real data applications will be presented.

Speaker Bio

Dr. Li-Pang Chen is an Associate Professor in the Department of Statistics at National Chengchi University (NCCU), Taiwan. He received his Ph.D. in Statistics from the University of Waterloo, Canada, in 2019, and subsequently held a postdoctoral fellowship at the University of Western Ontario from 2019 to 2020. His research focuses on developing and applying statistical methodologies in biostatistics, high-dimensional data analysis, measurement error models, and statistical machine learning. In addition to his research, Dr. Chen serves as a Guest Editor for the special issue Statistical Analysis and Data Science for Complex Data in the journal Mathematics, and as an Associate Editor for The New England Journal of Statistics in Data Science (Methodology Section). Further details are available on .