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Model Selection for Integrated Recovery/Recapture Data
Author(s) -
King R.,
Brooks S. P.
Publication year - 2002
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.2002.00841.x
Subject(s) - model selection , markov chain monte carlo , reversible jump markov chain monte carlo , computer science , bayesian probability , approximate bayesian computation , pairwise comparison , selection (genetic algorithm) , bayesian inference , mark and recapture , range (aeronautics) , posterior probability , econometrics , machine learning , data mining , artificial intelligence , mathematics , inference , population , materials science , demography , sociology , composite material
Summary. Catchpole et al. (1998, Biometrics 54, 33–46) provide a novel scheme for integrating both recovery and recapture data analyses and derive sufficient statistics that facilitate likelihood computations. In this article, we demonstrate how their efficient likelihood expression can facilitate Bayesian analyses of these kinds of data and extend their methodology to provide a formal framework for model determination. We consider in detail the issue of model selection with respect to a set of recapture/recovery histories of shags ( Phalacrocorax aristotelis ) and determine, from the enormous range of biologically plausible models available, which best describe the data. By using reversible jump Markov chain Monte Carlo methodology, we demonstrate how this enormous model space can be efficiently and effectively explored without having to resort to performing an infeasibly large number of pairwise comparisons or some ad hoc stepwise procedure. We find that the model used by Catchpole et al. (1998) has essentially zero posterior probability and that, of the 477,144 possible models considered, over 60% of the posterior mass is placed on three neighboring models with biologically interesting interpretations.