Greater Than the Sum of its Parts: Computationally Flexible Bayesian Hierarchical Modeling

Devin S. Johnson (), Brian M. Brost and Mevin B. Hooten
Additional contact information
Devin S. Johnson: National Marine Fisheries Service, NOAA
Brian M. Brost: National Marine Fisheries Service, NOAA
Mevin B. Hooten: The University of Texas at Austin

Journal of Agricultural, Biological and Environmental Statistics, 2022, vol. 27, issue 2, No 10, 382-400

Abstract: Abstract We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent normal distributions for the data component of the model. In the second stage, the Bayesian maximum a posteriori (MAP) estimator is found by maximizing the approximated posterior density with respect to the parameters. If the parameters of the model can be represented as normally distributed random effects, then the second-stage optimization is equivalent to fitting a multivariate normal linear mixed model. We consider a third stage that updates the estimates of distinct parameters for each data partition based on the results of the second stage. The method is demonstrated with two ecological data sets and models, a generalized linear mixed effects model (GLMM) and an integrated population model (IPM). The multistage results were compared to estimates from models fit in single stages to the entire data set. In both cases, multistage results were very similar to a full MCMC analysis. Supplementary materials accompanying this paper appear online.

Keywords: Approximation; Bayesian MAP estimation; Bayesian hierarchical model; Integrated population model; Linear mixed model; Meta-analysis; Multistage estimation (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13253-021-00485-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jagbes:v:27:y:2022:i:2:d:10.1007_s13253-021-00485-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/13253

DOI: 10.1007/s13253-021-00485-9

Access Statistics for this article

Journal of Agricultural, Biological and Environmental Statistics is currently edited by Stephen Buckland

More articles in Journal of Agricultural, Biological and Environmental Statistics from Springer, The International Biometric Society, American Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().