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Influence diagnostics for the Weibull model fit to censored data
dc.contributor.authorWeissfeld, Lisa A.en_US
dc.contributor.authorSchneider, Helmuten_US
dc.date.accessioned2006-04-10T13:52:38Z
dc.date.available2006-04-10T13:52:38Z
dc.date.issued1990-01en_US
dc.identifier.citationWeissfeld, Lisa A., Schneider, Helmut (1990/01)."Influence diagnostics for the Weibull model fit to censored data." Statistics &amp; Probability Letters 9(1): 67-73. <http://hdl.handle.net/2027.42/28787>en_US
dc.identifier.urihttp://www.sciencedirect.com/science/article/B6V1D-45DHJ42-Y/2/a3ce8e00bf279cfcdc4ee05a05676c1den_US
dc.identifier.urihttps://hdl.handle.net/2027.42/28787
dc.description.abstractMethods for detecting influential observations for the Weibull model fit to censored data are discussed. These methods include: one-step deletion diagnostics, influence functions and curvature diagnostics. Results indicate that the curvature diagnostics may be helpful in detecting masking.en_US
dc.format.extent587363 bytes
dc.format.extent3118 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.language.isoen_US
dc.publisherElsevieren_US
dc.titleInfluence diagnostics for the Weibull model fit to censored dataen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelSocial Sciencesen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USAen_US
dc.contributor.affiliationotherDepartment of Quantitative Business Analysis, Louisiana State University, Baton Rouge, LA 70803, USAen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/28787/1/0000621.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1016/0167-7152(90)90097-Qen_US
dc.identifier.sourceStatistics &amp; Probability Lettersen_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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