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Activity Number: 305 - Bayesian Modeling and Variable Selection Methods
Type: Contributed
Date/Time: Tuesday, July 30, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract #307378
Title: Incomplete High-Dimensional Inverse Covariance Estimation
Author(s): Yunxi Zhang* and Soeun Kim
Companies: University of Mississippi Medical Center and University of Texas Health Science Center at Houston
Keywords: High-Dimensional; Covariance matrix; Graphical Model; Missing Data; Imputation

The regularized estimation of the inverse covariance matrix has aroused wide attention for Gaussian graphical model. For high-dimensional data, the missing data are inevitable and should be handled carefully. We present a Bayesian algorithm for missing data imputation in such setting, and show the advantage of the proposed algorithm through simulations comparing with available approaches. We apply the algorithm on genetics data to estimate the covariance matrix.

Authors who are presenting talks have a * after their name.

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