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

TitleA beta-binomial model for estimating the size of a heterogeneous population
Authors
KeywordsBeta-binomial
Capture-recapture
Conditional maximum likelihood estimate
Gibbs sampler
Maximum likelihood estimate
Metropolis-Hastings algorithm
Issue Date2005
PublisherBlackwell Publishing Asia. The Journal's web site is located at http://www.blackwellpublishing.com/journals/ANZS
Citation
Australian And New Zealand Journal Of Statistics, 2005, v. 47 n. 3, p. 299-308 How to Cite?
AbstractThis 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. © 2005 Australian Statistical Publishing Association Inc.
Persistent Identifierhttp://hdl.handle.net/10722/82824
ISSN
2015 Impact Factor: 0.431
2015 SCImago Journal Rankings: 0.275
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorYip, PSFen_HK
dc.contributor.authorLiqun, XIen_HK
dc.contributor.authorArnold, Ren_HK
dc.contributor.authorHayakawa, YUen_HK
dc.date.accessioned2010-09-06T08:33:49Z-
dc.date.available2010-09-06T08:33:49Z-
dc.date.issued2005en_HK
dc.identifier.citationAustralian And New Zealand Journal Of Statistics, 2005, v. 47 n. 3, p. 299-308en_HK
dc.identifier.issn1369-1473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82824-
dc.description.abstractThis 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. © 2005 Australian Statistical Publishing Association Inc.en_HK
dc.languageengen_HK
dc.publisherBlackwell Publishing Asia. The Journal's web site is located at http://www.blackwellpublishing.com/journals/ANZSen_HK
dc.relation.ispartofAustralian and New Zealand Journal of Statisticsen_HK
dc.subjectBeta-binomialen_HK
dc.subjectCapture-recaptureen_HK
dc.subjectConditional maximum likelihood estimateen_HK
dc.subjectGibbs sampleren_HK
dc.subjectMaximum likelihood estimateen_HK
dc.subjectMetropolis-Hastings algorithmen_HK
dc.titleA beta-binomial model for estimating the size of a heterogeneous populationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1369-1473&volume=74&issue=3&spage=299&epage=308&date=2005&atitle=A+beta-binomial+model+for+estimating+the+size+of+a+heterogeneous+population+en_HK
dc.identifier.emailYip, PSF: sfpyip@hku.hken_HK
dc.identifier.authorityYip, PSF=rp00596en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/j.1467-842X.2005.00395.xen_HK
dc.identifier.scopuseid_2-s2.0-26244438506en_HK
dc.identifier.hkuros124652en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-26244438506&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume47en_HK
dc.identifier.issue3en_HK
dc.identifier.spage299en_HK
dc.identifier.epage308en_HK
dc.identifier.isiWOS:000232137800005-
dc.publisher.placeAustraliaen_HK
dc.identifier.scopusauthoridYip, PSF=7102503720en_HK
dc.identifier.scopusauthoridLiqun, XI=8951135600en_HK
dc.identifier.scopusauthoridArnold, R=15070007200en_HK
dc.identifier.scopusauthoridHayakawa, YU=7201356213en_HK
dc.identifier.citeulike307738-

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