Seminar by Prof. Runze LI from Department of Statistics, Pennsylvania State University

Analysis of ultrahigh-dimensional data plays critical roles in big data analysis. Feature screening aims to quickly reduce the dimensionality by filtering out irrelevant variables as many as possible without excluding out important variables. Thus, feature screening is an important statistical analytic tool for ultrahigh data. There have been many developments on this topic. In this talk, I will present general strategy and some applications of feature screening.

Runze Li is Eberly Family Chair Professor of Statistics at Penn State University. He received his Ph.D. in Statistics from University of North Carolina at Chapel Hill in 2000. He received his NSF Career Award in 2004. He is a fellow of IMS, ASA and AAAS. He was co-editor of Annals of Statistics, and served as associate editor of Annals of Statistics and Statistica Sinica. He currently serves as associate editor of JASA.