Covariance Estimation for Matrix Data Modeling
Wednesday, February 24 at 3:00 pm
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4:00 pm
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2021-02-24 23:00:00
2021-02-25 00:00:00
Covariance Estimation for Matrix Data Modeling
Department of Epidemiology & Biostatistics, Bioinformatics Presents:
Speaker: Weining Shen, PhD, Assistant Professor of Statistics, UC Irvine
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.
Department Of Epidemiology & Biostatistics
Fei.Jiang@ucsf.edu
America/Los_Angeles
public
Department of Epidemiology & Biostatistics, Bioinformatics Presents:
Speaker: Weining Shen, PhD, Assistant Professor of Statistics, UC Irvine
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.
