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.
| Original language | English |
|---|---|
| Pages (from-to) | 644-655 |
| Number of pages | 12 |
| Journal | Biometrics |
| Volume | 66 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2010 Jun 1 |
Keywords
- Bayesian model averaging
- Capture-recapture
- Closed populations
- Heterogeneity
- Mixture distribution
- Reversible jump MCMC
ASJC Scopus subject areas
- Statistics and Probability
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Agricultural and Biological Sciences(all)
- Applied Mathematics
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Cite this
Arnold, R., Hayakawa, Y., & Yip, P. (2010). Capture-recapture estimation using finite mixtures of arbitrary dimension. Biometrics, 66(2), 644-655. https://doi.org/10.1111/j.1541-0420.2009.01289.x