Activity Number:
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55
- Advances in Bayesian Sparse Regression
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Type:
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Contributed
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Date/Time:
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Monday, August 3, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #313737
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Title:
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A Generalized Likelihood Based Bayesian Approach for Scalable Joint Regression and Covariance Estimation in High Dimensions
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Author(s):
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Srijata Samanta* and Kshitij Khare and George Michailidis
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Companies:
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University of Florida and University of Florida and University of Florida
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Keywords:
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high-dimensional data;
Bayesian variable selection;
generalized likelihood;
multiple response linear regression
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Abstract:
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We consider a high-dimensional multiple response linear regression model with the focus being joint estimation of the regression and the dependence structure among the variables. We propose a generalized likelihood based Bayesian approach for computationally efficient joint estimation of the sparsity patterns in regression coefficients matrix B and the inverse covariance ? of the response variables. A procedure for obtaining parameter estimates of B and ? consistent with these sparsity patterns is also proposed. Finally we establish high-dimensional consistency of the proposed procedure for estimation of sparsity patterns of B and ? when the number of response variables and the number of predictors grow nearly exponentially with the sample size and also examine the performance of the approach based on synthetic data.
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Authors who are presenting talks have a * after their name.