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