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Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models
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Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent – Journal of Educational and Behavioral Statistics, 2015
When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix (S) of group-level varying coefficients are often degenerate. One can do better, even from…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Bayesian Statistics, Statistical Inference
Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models
Peer reviewed Peer reviewed
PDF on ERIC Download full text
Direct linkDirect link
Chung, Yeojin; Gelman, Andrew; Rabe-Hesketh, Sophia; Liu, Jingchen; Dorie, Vincent – Grantee Submission, 2015
When fitting hierarchical regression models, maximum likelihood (ML) estimation has computational (and, for some users, philosophical) advantages compared to full Bayesian inference, but when the number of groups is small, estimates of the covariance matrix [sigma] of group-level varying coefficients are often degenerate. One can do better, even…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Bayesian Statistics, Statistical Inference