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Article: Parametric regression models for continuous time removal and recapture studies

TitleParametric regression models for continuous time removal and recapture studies
Authors
KeywordsAnimal abundance
Capture-recapture experiment
Counting process
Heterogeneous capturability
Martingale
Population size estimation
Reliability testing
Issue Date1999
PublisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSB
Citation
Journal Of The Royal Statistical Society. Series B: Statistical Methodology, 1999, v. 61 n. 2, p. 401-411 How to Cite?
AbstractWe use a class of parametric counting process regression models that are commonly employed in the analysis of failure time data to formulate the subject-specific capture probabilities for removal and recapture studies conducted in continuous time. We estimate the regression parameters by modifying the conventional likelihood score function for left-truncated and right-censored data to accommodate an unknown population size and missing covariates on uncaptured subjects, and we subsequently estimate the population size by a martingale-based estimating function. The resultant estimators for the regression parameters and population size are consistent and asymptotically normal under appropriate regularity conditions. We assess the small sample properties of the proposed estimators through Monte Carlo simulation and we present an application to a bird banding exercise.
Persistent Identifierhttp://hdl.handle.net/10722/82733
ISSN
2015 Impact Factor: 4.222
2015 SCImago Journal Rankings: 7.429
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLin, DYen_HK
dc.contributor.authorYip, PSFen_HK
dc.date.accessioned2010-09-06T08:32:46Z-
dc.date.available2010-09-06T08:32:46Z-
dc.date.issued1999en_HK
dc.identifier.citationJournal Of The Royal Statistical Society. Series B: Statistical Methodology, 1999, v. 61 n. 2, p. 401-411en_HK
dc.identifier.issn1369-7412en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82733-
dc.description.abstractWe use a class of parametric counting process regression models that are commonly employed in the analysis of failure time data to formulate the subject-specific capture probabilities for removal and recapture studies conducted in continuous time. We estimate the regression parameters by modifying the conventional likelihood score function for left-truncated and right-censored data to accommodate an unknown population size and missing covariates on uncaptured subjects, and we subsequently estimate the population size by a martingale-based estimating function. The resultant estimators for the regression parameters and population size are consistent and asymptotically normal under appropriate regularity conditions. We assess the small sample properties of the proposed estimators through Monte Carlo simulation and we present an application to a bird banding exercise.en_HK
dc.languageengen_HK
dc.publisherWiley-Blackwell Publishing Ltd. The Journal's web site is located at http://www.blackwellpublishing.com/journals/RSSBen_HK
dc.relation.ispartofJournal of the Royal Statistical Society. Series B: Statistical Methodologyen_HK
dc.subjectAnimal abundanceen_HK
dc.subjectCapture-recapture experimenten_HK
dc.subjectCounting processen_HK
dc.subjectHeterogeneous capturabilityen_HK
dc.subjectMartingaleen_HK
dc.subjectPopulation size estimationen_HK
dc.subjectReliability testingen_HK
dc.titleParametric regression models for continuous time removal and recapture studiesen_HK
dc.typeArticleen_HK
dc.identifier.emailYip, PSF: sfpyip@hku.hken_HK
dc.identifier.authorityYip, PSF=rp00596en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1111/1467-9868.00184-
dc.identifier.scopuseid_2-s2.0-0033475340en_HK
dc.identifier.hkuros44316en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0033475340&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume61en_HK
dc.identifier.issue2en_HK
dc.identifier.spage401en_HK
dc.identifier.epage411en_HK
dc.identifier.isiWOS:000079074800008-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLin, DY=7403692293en_HK
dc.identifier.scopusauthoridYip, PSF=7102503720en_HK

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