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Article: A quasi-Bayesian analysis of regression outliers using Akaike's predictive likelihood

TitleA quasi-Bayesian analysis of regression outliers using Akaike's predictive likelihood
Authors
Fung, WK
KeywordsAkaike Information Criteria
Bayesian Method
Outliers
Predictive Likelihood
Issue Date1993
PublisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00362/index.htm
Citation
Statistical Papers, 1993, v. 34 n. 1, p. 133-141 How to Cite?
DOI: http://dx.doi.org/10.1007/BF02925535
AbstractThe Bayesian analysis of outliers using a non-informative prior for the parameters is non-trivial because models with different numbers of outliers have different dimensions. A quasi-Bayesian approach based on the Akaike's predictive likelihood is proposed for the analysis of regression outliers. It overcomes the dimensionality problem in Bayesian outlier analysis in which the likelihood of the outlier model is compensated by a correction factor adjusted for the number of outliers. The stack loss data set is analysed with satisfactory results. © 1993 Springer-Verlag.
Persistent Identifierhttp://hdl.handle.net/10722/172452
ISSN
0932-5026
2021 Impact Factor: 1.523
2020 SCImago Journal Rankings: 1.230
ISI Accession Number ID
WOS:A1993LC86000003

 

DC FieldValueLanguage
dc.contributor.authorFung, WKen_US
dc.date.accessioned2012-10-30T06:22:35Z-
dc.date.available2012-10-30T06:22:35Z-
dc.date.issued1993en_US
dc.identifier.citationStatistical Papers, 1993, v. 34 n. 1, p. 133-141en_US
dc.identifier.issn0932-5026en_US
dc.identifier.urihttp://hdl.handle.net/10722/172452-
dc.description.abstractThe Bayesian analysis of outliers using a non-informative prior for the parameters is non-trivial because models with different numbers of outliers have different dimensions. A quasi-Bayesian approach based on the Akaike's predictive likelihood is proposed for the analysis of regression outliers. It overcomes the dimensionality problem in Bayesian outlier analysis in which the likelihood of the outlier model is compensated by a correction factor adjusted for the number of outliers. The stack loss data set is analysed with satisfactory results. © 1993 Springer-Verlag.en_US
dc.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/00362/index.htmen_US
dc.relation.ispartofStatistical Papersen_US
dc.subjectAkaike Information Criteriaen_US
dc.subjectBayesian Methoden_US
dc.subjectOutliersen_US
dc.subjectPredictive Likelihooden_US
dc.titleA quasi-Bayesian analysis of regression outliers using Akaike's predictive likelihooden_US
dc.typeArticleen_US
dc.identifier.emailFung, WK: wingfung@hku.hken_US
dc.identifier.authorityFung, WK=rp00696en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1007/BF02925535en_US
dc.identifier.scopuseid_2-s2.0-52449143850en_US
dc.identifier.volume34en_US
dc.identifier.issue1en_US
dc.identifier.spage133en_US
dc.identifier.epage141en_US
dc.identifier.isiWOS:A1993LC86000003-
dc.publisher.placeGermanyen_US
dc.identifier.scopusauthoridFung, WK=13310399400en_US
dc.identifier.issnl0932-5026-

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