Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors-Reference-Cited by-同舟云学术

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Group Inverse-Gamma Gamma Shrinkage for Sparse Linear Models with Block-Correlated Regressors

Published:2023-01-01 Issue:-1 Volume:-1 Page:
ISSN:1936-0975
Container-title:Bayesian Analysis
language:
Short-container-title:Bayesian Anal.

Author:

Boss Jonathan¹,Datta Jyotishka²,Wang Xin³,Park Sung Kyun³,Kang Jian¹,Mukherjee Bhramar¹

Affiliation:

1. Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109

2. Department of Statistics, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061

3. Department of Epidemiology, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109

Publisher

Institute of Mathematical Statistics

Subject

Applied Mathematics,Statistics and Probability

Reference50 articles.

1. Kowal, D. R., Matteson, D. S., and Ruppert, D. (2019). “Dynamic shrinkage processes.” Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81(4): 781–804.

2. van der Pas, S., Szabó, B., and van der Vaart, A. (2017). “Uncertainty quantification for the horseshoe (with discussion).” Bayesian Analysis, 12(4): 1221–1274.

3. Zhang, Y. and Bondell, H. D. (2018). “Variable selection via penalized credible regions with Dirichlet–Laplace global-local shrinkage priors.” Bayesian Analysis, 13(3): 823–844.

4. Dawid, A. P. (1973). “Posterior expectations for large observations.” Biometrika, 60(3): 664–667.

5. Abramowitz, M. and Stegun, I. A. (1964). Handbook of mathematical functions with formulas, graphs, and mathematical tables, volume 55. US Government printing office.

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