Covariance Estimation for Matrix Data Analysis

Matrix-valued data has received an increasing interest in applications such as neuroscience, environmental studies and sports analytics. In this talk, I 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, I 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.

About the speaker

Weining Shen is associate professor of Statistics at the University of California, Irvine. He received his PhD from North Carolina State University in 2013, and his thesis won the Leonard J. Savage Dissertation Award. In 2013-2015, he was a postdoctoral fellow in Department of Biostatistics, M.D. Anderson Cancer Center. Dr. Shen’s research interest includes Bayesian methods, high-dimensional models, and applications in biology, disease studies and sports analytics. He currently serves as the AE for several journals including Statistica Sinica.