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 |
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Keywords
- Bayesian model averaging
- Capture-recapture
- Closed populations
- Heterogeneity
- Mixture distribution
- Reversible jump MCMC
ASJC Scopus subject areas
- Applied Mathematics
- Statistics and Probability
- Agricultural and Biological Sciences(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Immunology and Microbiology(all)
- Medicine(all)
Cite this
Capture-recapture estimation using finite mixtures of arbitrary dimension. / Arnold, Richard; Hayakawa, Yu; Yip, Paul.
In: Biometrics, Vol. 66, No. 2, 06.2010, p. 644-655.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Capture-recapture estimation using finite mixtures of arbitrary dimension
AU - Arnold, Richard
AU - Hayakawa, Yu
AU - Yip, Paul
PY - 2010/6
Y1 - 2010/6
N2 - 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.
AB - 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.
KW - Bayesian model averaging
KW - Capture-recapture
KW - Closed populations
KW - Heterogeneity
KW - Mixture distribution
KW - Reversible jump MCMC
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U2 - 10.1111/j.1541-0420.2009.01289.x
DO - 10.1111/j.1541-0420.2009.01289.x
M3 - Article
C2 - 19522870
AN - SCOPUS:77952988949
VL - 66
SP - 644
EP - 655
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 2
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