Description:

Department of Statistics 2020-2021 Seminar Series (joint with Biostatistics), Co-hosted by Women in Statistics (WIST) - Fall 2020

"Feature Selection with Survival Outcome Data"

Speaker: Hyokyoung (Grace) Hong, PhD, Associate Professor, Department of Statistics and Probability, Michigan State University

Abstract: Detecting biomarkers that are relevant to patients' survival outcomes is crucial for precision medicine. Dimension reduction is key in the process. Although regularization methods have been used for dimension reduction, they cannot handle a large number of candidate biomarkers generated by modern bio-techniques. Variable screening, which has been widely used for handling exceedingly large numbers of variables, is however much underdeveloped for censored outcome data. This talk introduces a series of new feature screening procedures that I have recently developed for survival data with ultrahigh dimensional covariates. These methods include conditional screening, integrated powered density screening, Lq-norm learning, and forward regression with partial likelihood. I will discuss the intuition behind and demonstrate their utilities through real data analyses.