STA/BST 290 Seminar: Anru Zhang

STA / BST 290 Seminar Series

Monday, January 12, 4:10pm, MSB 1143 (Statistics Seminar Room)
Refreshments at 3:30pm in MSB 4110 (Statistics Lounge)

Speaker: Anru Zhang (University of Pennsylvania)

Title: High-dimensional low-rank matrix recovery

Abstract: High-dimensional low-rank structure commonly arises in many applications including genomics, signal processing, and social science. In this talk, we will discuss some recent results on high-dimensional low-rank matrix recovery, including low-rank matrix recovery via rank-one projections and structured matrix completion. We provide theoretical justifications for the proposed methods and derive lower bounds for the estimation errors. The proposed estimators are shown to be rate-optimal under certain conditions. The methods are applied to integrate several ovarian cancer genomic studies, which enables us to construct more accurate prediction rules for ovarian cancer survival. Several extensions and related problems are also discussed.