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dc.contributor.authorYu, K-
dc.contributor.authorRubio, F-
dc.identifier.citationJournal of Applied Statistics, (2016)en_US
dc.description.abstractWe study objective Bayesian inference for linear regression models with residual errors distributed according to the class of two-piece scale mixtures of normal distributions. These models allow for capturing departures from the usual assumption of normality of the errors in terms of heavy tails, asymmetry, and certain types of heteroscedasticity. We propose a general noninformative, scale-invariant, prior structure and provide sufficient conditions for the propriety of the posterior distribution of the model parameters, which cover cases when the response variables are censored. These results allow us to apply the proposed models in the context of survival analysis. This paper represents an extension to the Bayesian framework of the models proposed in [19]. We present a simulation study that shows good frequentist properties of the posterior credible intervals as well as point estimators associated to the proposed priors. We illustrate the performance of these models with real data in the context of survival analysis of cancer patients.en_US
dc.publisherTaylor & Francisen_US
dc.subjectAccelerated failure time modelen_US
dc.subjectResidual lifeen_US
dc.subjectNoninformative prioren_US
dc.subjectTwo-piece distributionsen_US
dc.titleFlexible objective Bayesian linear regression with applications in survival analysisen_US
dc.relation.isPartOfJournal of Applied Statistics-
Appears in Collections:Dept of Mathematics Research Papers

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