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

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 languageEnglish
Pages (from-to)299-308
Number of pages10
JournalAustralian and New Zealand Journal of Statistics
Volume47
Issue number3
DOIs
Publication statusPublished - 2005 Sep 1
Externally publishedYes

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

Fingerprint Dive into the research topics of 'A beta-binomial model for estimating the size of a heterogeneous population'. Together they form a unique fingerprint.

Cite this