Zuur, Grietje; Garthwaite, Paul H. and Fryer, Rob J.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1002/1521-4036(200206)44:4%3C433::AID-BIMJ433%3E3.0.CO;2-4|
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This paper uses the analysis of a data set to examine a number of issues in Bayesian statistics and the application of MCMC methods. The data concern the selectivity of fishing nets and logistic regression is used to relate the size of a fish to the probability it will be retained or escape from a trawl net. Hierarchical models relate information from different trawls and posterior distributions are determined using MCMC. Centring data is shown to radically reduce autocorrelation in chains and Rao-Blackwellisation and chain-thinning are found to have little effect on parameter estimates. The results of four convergence diagnostics are compared and the sensitivity of the posterior distribution to the prior distribution is examined using a novel method. Nested models are fitted to the data and compared using intrinsic Bayes factors, pseudo-Bayes factors and credible intervals.
|Item Type:||Journal Article|
|Copyright Holders:||2002 Wiley-VCH Verlag Berlin GmbH|
|Keywords:||Bayes factors; chain thinning; convergence diagnostics; credible intervals; data centring; hierarchical models; logistic regression; MCMC; Rao-Blackwellisation; sensitivity analysis|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||11 Aug 2010 11:24|
|Last Modified:||04 Oct 2016 10:42|
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