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Article: Common structure in panels of short ecological time-series

TitleCommon structure in panels of short ecological time-series
Authors
KeywordsBootstrap
Canadian mink-muskrat data
Nonlinear time-series
Predator-prey interactions
Similarity measure
Issue Date2000
PublisherThe Royal Society. The Journal's web site is located at http://www.pubs.royalsoc.ac.uk/index.cfm?page=1087
Citation
Proceedings of the Royal Society B: Biological Sciences, 2000, v. 267 n. 1460, p. 2459-2467 How to Cite?
AbstractTypically, in many studies in ecology, epidemiology, biomedicine and others, we are confronted with panels of short time-series of which we are interested in obtaining a biologically meaningful grouping. Here, we propose a bootstrap approach to test whether the regression functions or the variances of the error terms in a family of stochastic regression models are the same. Our general setting includes panels of time-series models as a special case. We rigorously justify the use of the test by investigating its asymptotic properties, both theoretically and through simulations. The latter confirm that for finite sample size, bootstrap provides a better approximation than classical asymptotic theory. We then apply the proposed tests to the mink-muskrat data across 81 trapping regions in Canada. Ecologically interpretable groupings are obtained, which serve as a necessary first step before a fuller biological and statistical analysis of the food chain interaction.
Persistent Identifierhttp://hdl.handle.net/10722/49311
ISSN
2023 Impact Factor: 3.8
2023 SCImago Journal Rankings: 1.692
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYao, Qen_HK
dc.contributor.authorTong, Hen_HK
dc.contributor.authorFinkenstad, Ben_HK
dc.contributor.authorStenseth, NCen_HK
dc.date.accessioned2008-06-12T06:39:07Z-
dc.date.available2008-06-12T06:39:07Z-
dc.date.issued2000en_HK
dc.identifier.citationProceedings of the Royal Society B: Biological Sciences, 2000, v. 267 n. 1460, p. 2459-2467en_HK
dc.identifier.issn0962-8452en_HK
dc.identifier.urihttp://hdl.handle.net/10722/49311-
dc.description.abstractTypically, in many studies in ecology, epidemiology, biomedicine and others, we are confronted with panels of short time-series of which we are interested in obtaining a biologically meaningful grouping. Here, we propose a bootstrap approach to test whether the regression functions or the variances of the error terms in a family of stochastic regression models are the same. Our general setting includes panels of time-series models as a special case. We rigorously justify the use of the test by investigating its asymptotic properties, both theoretically and through simulations. The latter confirm that for finite sample size, bootstrap provides a better approximation than classical asymptotic theory. We then apply the proposed tests to the mink-muskrat data across 81 trapping regions in Canada. Ecologically interpretable groupings are obtained, which serve as a necessary first step before a fuller biological and statistical analysis of the food chain interaction.en_HK
dc.format.extent388 bytes-
dc.format.mimetypetext/html-
dc.languageengen_HK
dc.publisherThe Royal Society. The Journal's web site is located at http://www.pubs.royalsoc.ac.uk/index.cfm?page=1087en_HK
dc.relation.ispartofProceedings of the Royal Society B: Biological Sciences-
dc.subjectBootstrapen_HK
dc.subjectCanadian mink-muskrat dataen_HK
dc.subjectNonlinear time-seriesen_HK
dc.subjectPredator-prey interactionsen_HK
dc.subjectSimilarity measureen_HK
dc.titleCommon structure in panels of short ecological time-seriesen_HK
dc.typeArticleen_HK
dc.identifier.emailTong, H: h.tong@lse.ac.uken_HK
dc.description.naturelink_to_OA_fulltexten_HK
dc.identifier.doi10.1098/rspb.2000.1306en_HK
dc.identifier.pmid11133038-
dc.identifier.pmcidPMC1690833en_HK
dc.identifier.scopuseid_2-s2.0-0034619883-
dc.identifier.hkuros57105-
dc.identifier.volume267-
dc.identifier.issue1460-
dc.identifier.spage2459-
dc.identifier.epage2467-
dc.identifier.isiWOS:000166088300016-
dc.identifier.issnl0962-8452-

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