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Article: Continuous-time capture-recapture models with time variation and behavioural response

TitleContinuous-time capture-recapture models with time variation and behavioural response
Authors
KeywordsNon-Parametric Maximum Likelihood Estimator
Optimal Estimating Function
Population Size
Quasi-Likelihood
Issue Date2002
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, 2002, v. 44 n. 1, p. 41-54 How to Cite?
AbstractThis paper develops a likelihood-based inference procedure for continuous-time capture-recapture models. The first-capture and recapture intensities are assumed to be in constant proportion but may otherwise vary arbitrarily through time. The full likelihood is partitioned into two factors, one of which is analogous to the likelihood in a special type of multiplicative intensity model arising in failure time analysis. The remaining factor is free of the non-parametric nuisance parameter and is easily maximized. This factor provides an estimator of population size and an asymptotic variance under a counting process framework. The resulting estimation procedure is shown to be equivalent to that derived from a martingale-based estimating function approach. Simulation results are presented to examine the performance of the proposed estimators.
Persistent Identifierhttp://hdl.handle.net/10722/172075
ISSN
2015 Impact Factor: 0.431
2015 SCImago Journal Rankings: 0.275
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorHwang, WHen_US
dc.contributor.authorChao, Aen_US
dc.contributor.authorYip, PSFen_US
dc.date.accessioned2012-10-30T06:19:59Z-
dc.date.available2012-10-30T06:19:59Z-
dc.date.issued2002en_US
dc.identifier.citationAustralian And New Zealand Journal Of Statistics, 2002, v. 44 n. 1, p. 41-54en_US
dc.identifier.issn1369-1473en_US
dc.identifier.urihttp://hdl.handle.net/10722/172075-
dc.description.abstractThis paper develops a likelihood-based inference procedure for continuous-time capture-recapture models. The first-capture and recapture intensities are assumed to be in constant proportion but may otherwise vary arbitrarily through time. The full likelihood is partitioned into two factors, one of which is analogous to the likelihood in a special type of multiplicative intensity model arising in failure time analysis. The remaining factor is free of the non-parametric nuisance parameter and is easily maximized. This factor provides an estimator of population size and an asymptotic variance under a counting process framework. The resulting estimation procedure is shown to be equivalent to that derived from a martingale-based estimating function approach. Simulation results are presented to examine the performance of the proposed estimators.en_US
dc.languageengen_US
dc.publisherBlackwell Publishing Asia. The Journal's web site is located at http://www.blackwellpublishing.com/journals/ANZSen_US
dc.relation.ispartofAustralian and New Zealand Journal of Statisticsen_US
dc.subjectNon-Parametric Maximum Likelihood Estimatoren_US
dc.subjectOptimal Estimating Functionen_US
dc.subjectPopulation Sizeen_US
dc.subjectQuasi-Likelihooden_US
dc.titleContinuous-time capture-recapture models with time variation and behavioural responseen_US
dc.typeArticleen_US
dc.identifier.emailYip, PSF: sfpyip@hku.hken_US
dc.identifier.authorityYip, PSF=rp00596en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1111/1467-842X.00206-
dc.identifier.scopuseid_2-s2.0-0037677827en_US
dc.identifier.hkuros68109-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0037677827&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume44en_US
dc.identifier.issue1en_US
dc.identifier.spage41en_US
dc.identifier.epage54en_US
dc.identifier.isiWOS:000173983200005-
dc.publisher.placeAustraliaen_US
dc.identifier.scopusauthoridHwang, WH=7402323207en_US
dc.identifier.scopusauthoridChao, A=7102703038en_US
dc.identifier.scopusauthoridYip, PSF=7102503720en_US

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