Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models /

Weakly Informative Prior for Point Estimation of Covariance Matrices in Hierarchical Models / Yeojin Chung, Andrew Gelman and Sophia Rabe-Hesketh.

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 coefficient...

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Bibliographic Details
Online Access: Full Text (via ERIC)
Main Authors: Chung, Yeojin, Gelman, Andrew (Author), Rabe-Hesketh, S. (Author), Liu, Jingchen (Author), Dorie, Vincent (Author)
Format: eBook
Language:English
Published: [Place of publication not identified] : Distributed by ERIC Clearinghouse, 2015.
Subjects:
Regression (Statistics)
Hierarchical Linear Modeling.
Bayesian Statistics.
Statistical Inference.
Computation.
Matrices.
Statistical Analysis.
Maximum Likelihood Statistics.
Educational Research.

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Call Number: ED590309
ED590309 Available

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