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Article: Influence analysis for linear measurement error models

TitleInfluence analysis for linear measurement error models
Authors
KeywordsCorrected likelihood
Diagnostics
Global influence
Local influence
Measurement error models
Issue Date2000
PublisherSpringer Verlag.
Citation
Annals Of The Institute Of Statistical Mathematics, 2000, v. 52 n. 2, p. 367-379 How to Cite?
AbstractIn this paper, we present a unified diagnostic method for linear measurement error models based upon the corrected likelihood of Nakamura, (1990, Biometrika, 77, 127-137). Both global influence and local influence are discussed. The case-deletion model and mean-shift outlier model are considered, and they are shown to be approximately equivalent. Several diagnostic measures are derived and discussed. It is found that they can be written in terms of the residual and leverage measure. Some existing results are improved. Numerical example illustrates that our method is useful for diagnosing influential observations.
Persistent Identifierhttp://hdl.handle.net/10722/83005
ISSN
2015 Impact Factor: 0.768
2015 SCImago Journal Rankings: 0.931
References

 

DC FieldValueLanguage
dc.contributor.authorZhong, XPen_HK
dc.contributor.authorWei, BCen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2010-09-06T08:35:51Z-
dc.date.available2010-09-06T08:35:51Z-
dc.date.issued2000en_HK
dc.identifier.citationAnnals Of The Institute Of Statistical Mathematics, 2000, v. 52 n. 2, p. 367-379en_HK
dc.identifier.issn0020-3157en_HK
dc.identifier.urihttp://hdl.handle.net/10722/83005-
dc.description.abstractIn this paper, we present a unified diagnostic method for linear measurement error models based upon the corrected likelihood of Nakamura, (1990, Biometrika, 77, 127-137). Both global influence and local influence are discussed. The case-deletion model and mean-shift outlier model are considered, and they are shown to be approximately equivalent. Several diagnostic measures are derived and discussed. It is found that they can be written in terms of the residual and leverage measure. Some existing results are improved. Numerical example illustrates that our method is useful for diagnosing influential observations.en_HK
dc.languageengen_HK
dc.publisherSpringer Verlag.en_HK
dc.relation.ispartofAnnals of the Institute of Statistical Mathematicsen_HK
dc.subjectCorrected likelihooden_HK
dc.subjectDiagnosticsen_HK
dc.subjectGlobal influenceen_HK
dc.subjectLocal influenceen_HK
dc.subjectMeasurement error modelsen_HK
dc.titleInfluence analysis for linear measurement error modelsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0020-3157&volume=52&spage=367&epage=379&date=2000&atitle=Influence+analysis+for+linear+measurement+error+modelsen_HK
dc.identifier.emailFung, WK: wingfung@hku.hken_HK
dc.identifier.authorityFung, WK=rp00696en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.scopuseid_2-s2.0-6744243279en_HK
dc.identifier.hkuros55964en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-6744243279&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume52en_HK
dc.identifier.issue2en_HK
dc.identifier.spage367en_HK
dc.identifier.epage379en_HK
dc.publisher.placeGermanyen_HK
dc.identifier.scopusauthoridZhong, XP=7202160440en_HK
dc.identifier.scopusauthoridWei, BC=7202263644en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK

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