Publication:
On the Asymptotic Distribution of Cook’s distance in Logistic Regression Models

dc.contributor.authorMartín Apaolaza, Níriam
dc.contributor.authorPardo Llorente, Leandro
dc.date.accessioned2023-06-20T01:24:09Z
dc.date.available2023-06-20T01:24:09Z
dc.date.issued2009-09-24
dc.description.abstractIt sometimes occurs that one or more components of the data exert a disproportionate influence on the model estimation. We need a reliable tool for identifying such troublesome cases in order to decide either eliminate from the sample, when the data collect was badly realized, or otherwise take care on the use of the model because the results could be affected by such components. Since a measure for detecting influential cases in linear regression setting was proposed by Cook [Detection of influential observations in linear regression, Technometrics 19 (1977), pp. 15–18.], apart from the same measure for other models, several new measures have been suggested as single-case diagnostics. For most of them some cutoff values have been recommended (see [D.A. Belsley, E. Kuh, and R.E. Welsch, Regression Diagnostics: Identifying Influential Data and Sources of Collinearity, 2nd ed., John Wiley & Sons, New York, Chichester, Brisban, (2004).], for instance), however the lack of a quantile type cutoff for Cook's statistics has induced the analyst to deal only with index plots as worthy diagnostic tools. Focussed on logistic regression, the aim of this paper is to provide the asymptotic distribution of Cook's distance in order to look for a meaningful cutoff point for detecting influential and leverage observations.
dc.description.departmentDepto. de Estadística e Investigación Operativa
dc.description.facultyFac. de Ciencias Matemáticas
dc.description.refereedFALSE
dc.description.sponsorshipMinisterio de Educación y Ciencia
dc.description.sponsorshipComunidad de Madrid; Universidad Complutense de Madrid
dc.description.statuspub
dc.eprint.idhttps://eprints.ucm.es/id/eprint/77035
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dc.identifier.doi10.1080/02664760802562498
dc.identifier.issn0266-4763
dc.identifier.officialurlhttps://doi.org/10.1080/02664760802562498
dc.identifier.relatedurlhttps://www.tandfonline.com/doi/full/10.1080/02664760802562498
dc.identifier.urihttps://hdl.handle.net/20.500.14352/43572
dc.issue.number10
dc.journal.titleJournal of Applied Statistics
dc.language.isoeng
dc.page.final1146
dc.page.initial1119
dc.publisherTaylor & Francis
dc.relation.projectIDMTM2006-06872
dc.relation.projectIDCAM-UCM2007-910707
dc.rightsAtribución-NoComercial 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by-nc/3.0/es/
dc.subject.cdu519.233.5
dc.subject.keywordCook's distance
dc.subject.keywordLogistic regression
dc.subject.keywordMaximum likelihood estimation
dc.subject.keywordOutlier
dc.subject.keywordLeverage
dc.subject.ucmEstadística matemática (Matemáticas)
dc.subject.unesco1209 Estadística
dc.titleOn the Asymptotic Distribution of Cook’s distance in Logistic Regression Models
dc.typejournal article
dc.volume.number36
dspace.entity.typePublication
relation.isAuthorOfPublicationa6409cba-03ce-4c3b-af08-e673b7b2bf58
relation.isAuthorOfPublication.latestForDiscoverya6409cba-03ce-4c3b-af08-e673b7b2bf58
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