title: Capture-recapture estimation using finite mixtures of arbitrary dimension.
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.
authors.affiliation: School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, New Zealand. richard.arnold@vuw.ac.nz
authors.forename: Richard
authors.initials: R
authors.lastname: Arnold
authors.forename: Yu
authors.initials: Y
authors.lastname: Hayakawa
authors.forename: Paul
authors.initials: P
authors.lastname: Yip
mesh_terms: D000818:Animals; D001699:Biometry / Q000379:methods*; D004463:Ecology / Q000706:statistics & numerical data; D008390:Markov Chains; D009010:Monte Carlo Method; D011336:Probability; D011817:Rabbits