Covariance Estimation for Matrix Data Modeling

February 24, 2021
3:00 - 4:00pm
Zoom - Registry Link Below

Matrix-valued data has received increasing interest in applications such as neuroscience, environmental studies and sports analytics. Shen will discuss a recent project on estimating the covariance of matrix data. Unlike previous works that rely heavily on matrix normal distribution assumption and the requirement of fixed matrix size, he will introduce a class of distribution-free regularized covariance estimation methods for high-dimensional matrix data under a separability condition and a bandable covariance structure. Computational algorithms, theoretical results, and applications will be discussed.



Speaker: Weining Shen, PhD, Assistant Professor of Statistics, UC Irvine






Event Type: 
Biostatistics and Bioinformatics Seminar