A beta-binomial model for estimating the size of a heterogeneous population

Paul S.F. Yip; X. I. Liqun; Richard Arnold; Y. U. Hayakawa

doi:10.1111/j.1467-842X.2005.00395.x

A beta-binomial model for estimating the size of a heterogeneous population

Paul S.F. Yip^*, X. I. Liqun, Richard Arnold, Y. U. Hayakawa

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

Abstract

This paper compares the properties of various estimators for a beta-binomial model for estimating the size of a heterogeneous population. It is found that maximum likelihood and conditional maximum likelihood estimators perform well for a large population with a large capture proportion. The jackknife and the sample coverage estimators are biased for low capture probabilities. The performance of the martingale estimator is satisfactory, but it requires full capture histories. The Gibbs sampler and Metropolis-Hastings algorithm provide reasonable posterior estimates for informative priors.

Original language	English
Pages (from-to)	299-308
Number of pages	10
Journal	Australian and New Zealand Journal of Statistics
Volume	47
Issue number	3
DOIs	https://doi.org/10.1111/j.1467-842X.2005.00395.x
Publication status	Published - 2005 Sept 1
Externally published	Yes

Keywords

Beta-binomial
Capture-recapture
Conditional maximum likelihood estimate
Gibbs sampler
Maximum likelihood estimate
Metropolis-Hastings algorithm

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1111/j.1467-842X.2005.00395.x

Cite this

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abstract = "This paper compares the properties of various estimators for a beta-binomial model for estimating the size of a heterogeneous population. It is found that maximum likelihood and conditional maximum likelihood estimators perform well for a large population with a large capture proportion. The jackknife and the sample coverage estimators are biased for low capture probabilities. The performance of the martingale estimator is satisfactory, but it requires full capture histories. The Gibbs sampler and Metropolis-Hastings algorithm provide reasonable posterior estimates for informative priors.",

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AU - Hayakawa, Y. U.

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N2 - This paper compares the properties of various estimators for a beta-binomial model for estimating the size of a heterogeneous population. It is found that maximum likelihood and conditional maximum likelihood estimators perform well for a large population with a large capture proportion. The jackknife and the sample coverage estimators are biased for low capture probabilities. The performance of the martingale estimator is satisfactory, but it requires full capture histories. The Gibbs sampler and Metropolis-Hastings algorithm provide reasonable posterior estimates for informative priors.

AB - This paper compares the properties of various estimators for a beta-binomial model for estimating the size of a heterogeneous population. It is found that maximum likelihood and conditional maximum likelihood estimators perform well for a large population with a large capture proportion. The jackknife and the sample coverage estimators are biased for low capture probabilities. The performance of the martingale estimator is satisfactory, but it requires full capture histories. The Gibbs sampler and Metropolis-Hastings algorithm provide reasonable posterior estimates for informative priors.

KW - Beta-binomial

KW - Capture-recapture

KW - Conditional maximum likelihood estimate

KW - Gibbs sampler

KW - Maximum likelihood estimate

KW - Metropolis-Hastings algorithm

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JO - Australian and New Zealand Journal of Statistics

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A beta-binomial model for estimating the size of a heterogeneous population

Abstract

Keywords

ASJC Scopus subject areas

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