Graphical diagnostics methods based on generalized residuals for logistic regression models

Simple item page
dc.contributor.authorPardo, M.C.
dc.contributor.authorViviani García, Paola
dc.date.accessioned2023-06-30T20:35:10Z
dc.date.available2023-06-30T20:35:10Z
dc.date.issued2012
dc.description.abstractThe logistic regression model is remarkably flexible. Nevertheless, lack of fit may also occur so to be able to detect departures from the model is important. In this paper, a generalized empirical probability plot based on generalized residuals is developed. The method is illustrated with simulated and real data.
dc.fechaingreso.objetodigital2023-06-13
dc.format.extent18 páginas
dc.fuente.origenORCID
dc.identifier.issn1012-9367
dc.identifier.urihttp://www.scopus.com/inward/record.url?eid=2-s2.0-84867713132&partnerID=MN8TOARS
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/74010
dc.information.autorucEscuela de Medicina ; Viviani García, Paola ; 0000-0003-2886-5228 ; 729
dc.issue.numero4
dc.language.isoen
dc.nota.accesoContenido parcial
dc.pagina.final512
dc.pagina.inicio495
dc.revistaPakistan Journal of Statistics
dc.rightsacceso restringido
dc.titleGraphical diagnostics methods based on generalized residuals for logistic regression models
dc.typeartículo
dc.volumen28
sipa.codpersvinculados729
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Graphical diagnostics methods based on generalized residuals for logistic regression models.pdf
Size:
2.16 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Artículos de conferencia