Multivariate Global-Local Priors for Small Area Estimation
Affiliation
Department of Mathematics, University of ArizonaIssue Date
2022Keywords
concentration inequalitiesglobal-local prior
hierarchical multivariate model
posterior density
shrinkage estimators
small area estimation
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MDPICitation
Ghosh, T., Ghosh, M., Maples, J. J., & Tang, X. (2022). Multivariate Global-Local Priors for Small Area Estimation. Stats, 5(3), 673–688.Journal
StatsRights
Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
It is now widely recognized that small area estimation (SAE) needs to be model-based. Global-local (GL) shrinkage priors for random effects are important in sparse situations where many areas’ level effects do not have a significant impact on the response beyond what is offered by covariates. We propose in this paper a hierarchical multivariate model with GL priors. We prove the propriety of the posterior density when the regression coefficient matrix has an improper uniform prior. Some concentration inequalities are derived for the tail probabilities of the shrinkage estimators. The proposed method is illustrated via both data analysis and simulations. © 2022 by the authors.Note
Open access journalISSN
2571-905XVersion
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Except where otherwise noted, this item's license is described as Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).