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Article: Confirmation of multiple outliers in generalized linear and nonlinear regressions

TitleConfirmation of multiple outliers in generalized linear and nonlinear regressions
Authors
Lee, AHFung, WK
KeywordsGeneralized linear model
High breakdown methods
Multiple outliers
Nonlinear regression
Simulated envelopes
Issue Date1997
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csda
Citation
Computational Statistics And Data Analysis, 1997, v. 25 n. 1, p. 55-65 How to Cite?
AbstractWe propose a stepwise procedure for the detection of multiple outliers in generalized linear models and nonlinear regressions. The algorithm starts with a high breakdown point estimation method to find the potential outliers, then uses an adding-back iterative approach to confirm such outliers with envelopes derived from simulation. The proposed method can overcome the masking and swamping problems commonly occurred in multiple outliers detection. We apply the procedure to two real examples, and satisfactory results are obtained. © 1997 Elsevier Science B.V.
Persistent Identifierhttp://hdl.handle.net/10722/82780
ISSN
0167-9473
2023 Impact Factor: 1.5
2023 SCImago Journal Rankings: 1.008
ISI Accession Number ID
WOS:A1997XK58400005
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorLee, AHen_HK
dc.contributor.authorFung, WKen_HK
dc.date.accessioned2010-09-06T08:33:20Z-
dc.date.available2010-09-06T08:33:20Z-
dc.date.issued1997en_HK
dc.identifier.citationComputational Statistics And Data Analysis, 1997, v. 25 n. 1, p. 55-65en_HK
dc.identifier.issn0167-9473en_HK
dc.identifier.urihttp://hdl.handle.net/10722/82780-
dc.description.abstractWe propose a stepwise procedure for the detection of multiple outliers in generalized linear models and nonlinear regressions. The algorithm starts with a high breakdown point estimation method to find the potential outliers, then uses an adding-back iterative approach to confirm such outliers with envelopes derived from simulation. The proposed method can overcome the masking and swamping problems commonly occurred in multiple outliers detection. We apply the procedure to two real examples, and satisfactory results are obtained. © 1997 Elsevier Science B.V.en_HK
dc.languageengen_HK
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/csdaen_HK
dc.relation.ispartofComputational Statistics and Data Analysisen_HK
dc.rightsComputational Statistics & Data Analysis. Copyright © Elsevier BV.en_HK
dc.subjectGeneralized linear modelen_HK
dc.subjectHigh breakdown methodsen_HK
dc.subjectMultiple outliersen_HK
dc.subjectNonlinear regressionen_HK
dc.subjectSimulated envelopesen_HK
dc.titleConfirmation of multiple outliers in generalized linear and nonlinear regressionsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0167-9473&volume=25&spage=55&epage=65&date=1997&atitle=Confirmation+of+multiple+outliers+in+generalized+linear+and+nonlinear+regressionsen_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-s2.0-0031551140en_HK
dc.identifier.hkuros26783en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-0031551140&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume25en_HK
dc.identifier.issue1en_HK
dc.identifier.spage55en_HK
dc.identifier.epage65en_HK
dc.identifier.isiWOS:A1997XK58400005-
dc.publisher.placeNetherlandsen_HK
dc.identifier.scopusauthoridLee, AH=26643271800en_HK
dc.identifier.scopusauthoridFung, WK=13310399400en_HK
dc.identifier.issnl0167-9473-

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