Publicación:
Characteristics of two CompetingRisksModelswith Weibull Distributed Risks

dc.contributor.authorYáñez, Sergio
dc.contributor.authorEscobar, Luis A.
dc.contributor.authorGonzález, Nelfi
dc.contributor.corporatenameAcademia Colombiana de Ciencias Exactas, Físicas y Naturalesspa
dc.date.accessioned2021-10-15T15:21:07Z
dc.date.available2021-10-15T15:21:07Z
dc.date.issued2014-10-07
dc.description.abstractLifetime and survival data are usually the lives of subjects, units, or systems that has been exposed to multiple risks or modes of failure. In the analysis of the data, however, is common to ignore the modes of failure because they are unknown, they are not recorded, or because the complexity of including them in the modeling. It is of interest knowing when the conclusions might be robust to ignoring the failure modes in the analysis. In particular, it would be useful to characterize situations where it could be safely said that the modes of failure effect on the analysis would be negligible or that failing to include such information could completely invalidate the conclusions drawn from the study. As a first step in identifying when the failure modes have little or large influence on the competing risks model, this article studies two different competing risks models: (a) a model with independent risks; (b) a model derived from a multivariate Weibull with dependence.eng
dc.description.abstractLos datos de tiempos de falla y sobrevivencia se refieren generalmente a la vida de individuos, unidades o sistemas que han sido expuestos a múltiples riesgos o modos de falla. Sin embargo, en el análisis de los datos es común ignorar los modos de falla porque son desconocidos, no se registran o por la complejidad de la modelación al incluirlos. Es importante, por lo tanto, determinar la robustez del análisis ignorando los modos de falla. En particular, sería útil caracterizar situaciones donde pudiera decirse que el efecto de los modos de falla en el análisis es despreciable o donde no incluir tal información pudiera invalidar completamente las inferencias del estudio. Como un primer paso para identificar cuando los modos de falla tienen poca o mucha influencia en el modelo de riesgos en competencia, este artículo estudia dos modelos de riesgos en competencia: (a) un modelo con riesgos independientes; (b) un modelo derivado de una distribución Weibull multivariada con dependencia.spa
dc.format.extent14 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.18257/raccefyn.130
dc.identifier.urihttps://repositorio.accefyn.org.co/handle/001/813
dc.language.isoengspa
dc.publisherAcademia Colombiana de Ciencias Exactas, Físicas y Naturalesspa
dc.publisher.placeBogotá, Colombiaspa
dc.relation.citationendpage311spa
dc.relation.citationissue148spa
dc.relation.citationstartpage298spa
dc.relation.citationvolume38spa
dc.relation.ispartofjournalRevista de la Academia Colombiana de Ciencias Exactas, Físicas y Naturalesspa
dc.rightsCreative Commons Attribution-NonCommercial-ShareAlike 4.0 Internationalspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.licenseAtribución-NoComercial 4.0 Internacional (CC BY-NC 4.0)spa
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/spa
dc.subject.proposalRiesgos en competenciaspa
dc.subject.proposalCompeting riskseng
dc.subject.proposalGráficos de probabilidadspa
dc.subject.proposalProbability plotseng
dc.subject.proposalFamilia de log-localización y escalaspa
dc.subject.proposalLog-location-scale familyeng
dc.subject.proposalDistribución Weibullspa
dc.subject.proposalWeibull distributioneng
dc.subject.proposalDistribución Weibull multivariada con dependenciaspa
dc.subject.proposalMultivariate Weibull with dependenceeng
dc.subject.proposalDistribución lognormalspa
dc.subject.proposalLognormal distributioneng
dc.titleCharacteristics of two CompetingRisksModelswith Weibull Distributed Risksspa
dc.typeArtículo de revistaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.coarversionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.contentDataPaperspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTREVspa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa
dcterms.audienceEstudiantes, Profesores, Comunidad científicaspa
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dcterms.referencesLu, J. C. and G. K. Bhattacharyya (1990). Some new constructions of bivariate Weibull models. Annals of the Institute of Statistical Mathematics 42(3): 543–559.spa
dcterms.referencesMeeker, W. Q. and L. A. Escobar (1998). Statistical Methods for Reliability Data. New York: John Wiley & Sons.spa
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