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Capture-recapture estimation using finite mixtures of arbitrary dimension
Richard Arnold1, Yu Hayakawa, Paul Yip
1School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, New Zealand. richard.arnold@vuw.ac.nz
Abstract:
Reversible jump Markov chain Monte Carlo (RJMCMC) methods are used to fit Bayesian capture-recapture models incorporating heterogeneity in individuals and samples. Heterogeneity in capture probabilities comes from finite mixtures and/or fixed sample effects allowing for interactions. Estimation by RJMCMC allows automatic model selection and/or model averaging. Priors on the parameters stabilize the estimates and produce realistic credible intervals for population size for overparameterized models, in contrast to likelihood-based methods. To demonstrate the approach we analyze the standard Snowshoe hare and Cottontail rabbit data sets from ecology, a reliability testing data set.
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