dc.creatorORTEGA, Edwin M. M.
dc.creatorCANCHO, Vicente G.
dc.creatorPAULA, Gilberto A.
dc.date.accessioned2012-10-20T03:34:58Z
dc.date.accessioned2018-07-04T15:38:45Z
dc.date.available2012-10-20T03:34:58Z
dc.date.available2018-07-04T15:38:45Z
dc.date.created2012-10-20T03:34:58Z
dc.date.issued2009
dc.identifierLIFETIME DATA ANALYSIS, v.15, n.1, p.79-106, 2009
dc.identifier1380-7870
dc.identifierhttp://producao.usp.br/handle/BDPI/28955
dc.identifier10.1007/s10985-008-9096-y
dc.identifierhttp://dx.doi.org/10.1007/s10985-008-9096-y
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1625597
dc.description.abstractIn this paper, the generalized log-gamma regression model is modified to allow the possibility that long-term survivors may be present in the data. This modification leads to a generalized log-gamma regression model with a cure rate, encompassing, as special cases, the log-exponential, log-Weibull and log-normal regression models with a cure rate typically used to model such data. The models attempt to simultaneously estimate the effects of explanatory variables on the timing acceleration/deceleration of a given event and the surviving fraction, that is, the proportion of the population for which the event never occurs. The normal curvatures of local influence are derived under some usual perturbation schemes and two martingale-type residuals are proposed to assess departures from the generalized log-gamma error assumption as well as to detect outlying observations. Finally, a data set from the medical area is analyzed.
dc.languageeng
dc.publisherSPRINGER
dc.relationLifetime Data Analysis
dc.rightsCopyright SPRINGER
dc.rightsrestrictedAccess
dc.subjectCure-fraction models
dc.subjectGeneralized log-gamma distribution
dc.subjectSensitivity analysis
dc.subjectResidual analysis
dc.subjectLifetime data
dc.titleGeneralized log-gamma regression models with cure fraction
dc.typeArtículos de revistas


Este ítem pertenece a la siguiente institución