dc.creatorOrtega E.M.M.
dc.creatorLemonte A.J.
dc.creatorCordeiro G.M.
dc.creatorCancho V.G.
dc.creatorMialhe F.L.
dc.date2017
dc.date2017-08-17T19:09:30Z
dc.date2017-08-17T19:09:30Z
dc.date.accessioned2018-03-29T05:14:36Z
dc.date.available2018-03-29T05:14:36Z
dc.identifierJournal Of Applied Statistics. Taylor And Francis Ltd., p. 1 - 25, 2017.
dc.identifier0266-4763
dc.identifier10.1080/02664763.2016.1277192
dc.identifierhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85008676991&doi=10.1080%2f02664763.2016.1277192&partnerID=40&md5=ff595acbed20188c502eaca3a814b9a4
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/322858
dc.identifier2-s2.0-85008676991
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1357021
dc.descriptionWe introduce a new class of heteroscedastic log-exponentiated Weibull (LEW) regression models. The class of regression models can be applied to censored data and be used more effectively in survival analysis. Maximum likelihood estimation of the model parameters with censored data as well as influence diagnostics for the new regression model is investigated. For different parameter settings, sample sizes and censoring percentages, various simulation studies are performed and compared to the performance of the heteroscedastic LEW regression model. The normal curvatures for studying local influence are derived under various perturbation schemes. An empirical application to a real data set is provided to illustrate the usefulness of the new class of heteroscedastic regression models. © 2017 Informa UK Limited, trading as Taylor & Francis Group
dc.description1
dc.description25
dc.languageEnglish
dc.publisherTaylor and Francis Ltd.
dc.relationJournal of Applied Statistics
dc.rightsfechado
dc.sourceScopus
dc.subjectExponentiated Weibull Distribution
dc.subjectLifetime Data
dc.subjectLocation - Scale Regression Model
dc.subjectLog - Weibull Regression Model
dc.titleHeteroscedastic Log-exponentiated Weibull Regression Model
dc.typeArtículos de revistas


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