Objective Bayesian analysis of spatial models with
separable correlation functions
Objective Bayesian analysis of spatial models with
separable correlation functions
This paper considers general linear models for Gaussian geostatistical data with multi-dimensional separable
correlation functions involving multiple parameters. We derive various objective priors, such as the Jeffreys-rule, independence Jeffreys, and usual and exact reference priors for the model parameters. In addition, we relax and simplify
the assumptions in Paulo [2005] for the propriety of the posteriors in the general setup. We show that the frequentist coverage of posterior credible intervals for a function of range parameters do not depend on the regression coefficient or error variance. These objective priors and a proper flat prior based on ML estimates are compared by examining the frequentist coverage of equal-tailed Bayesian credible intervals. An illustrative example is given from the field of complex computer model validations.
Ren, Cuirong
8e01198e-2924-4198-becc-66eb90a943ba
Sun, Dongchu
ff6a767c-6bd3-450f-9bf6-a43deb2227b8
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Ren, Cuirong
8e01198e-2924-4198-becc-66eb90a943ba
Sun, Dongchu
ff6a767c-6bd3-450f-9bf6-a43deb2227b8
Sahu, Sujit K.
33f1386d-6d73-4b60-a796-d626721f72bf
Ren, Cuirong, Sun, Dongchu and Sahu, Sujit K.
(2013)
Objective Bayesian analysis of spatial models with
separable correlation functions.
Canadian Journal of Statistics.
(Submitted)
Abstract
This paper considers general linear models for Gaussian geostatistical data with multi-dimensional separable
correlation functions involving multiple parameters. We derive various objective priors, such as the Jeffreys-rule, independence Jeffreys, and usual and exact reference priors for the model parameters. In addition, we relax and simplify
the assumptions in Paulo [2005] for the propriety of the posteriors in the general setup. We show that the frequentist coverage of posterior credible intervals for a function of range parameters do not depend on the regression coefficient or error variance. These objective priors and a proper flat prior based on ML estimates are compared by examining the frequentist coverage of equal-tailed Bayesian credible intervals. An illustrative example is given from the field of complex computer model validations.
This record has no associated files available for download.
More information
Submitted date: June 2013
Organisations:
Statistical Sciences Research Institute
Identifiers
Local EPrints ID: 353918
URI: http://eprints.soton.ac.uk/id/eprint/353918
ISSN: 0319-5724
PURE UUID: dad2b9d2-d8a9-43d6-bc7e-902227754c16
Catalogue record
Date deposited: 25 Jun 2013 09:18
Last modified: 23 Feb 2023 02:42
Export record
Contributors
Author:
Cuirong Ren
Author:
Dongchu Sun
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics
