Bayesian semiparametric inference for the accelerated failure time model using hierarchical mixture modeling with N-IG priors

Raffaele Argiento, GUGLIELMI A., PIEVATOLO A., F RUGGERI

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We will pursue a Bayesian semiparametric approach for an Accelerated Failure Time regression model, usually consid- ered in survival analysis, when the error distribution is a mix- ture of parametric densities with a nonparametric mixing mea- sure. The Dirichlet process is a popular choice for the mix- ing measure, yielding a Dirichlet process mixture model for the error; the paper considers the same model, but here, as an alternative to the Dirichlet process, the mixing measure is equal to a normalized inverse-Gaussian prior, built from nor- malized inverse-gaussian finite dimensional distributions, as recently proposed in the literature. A comparison between the two models will be carried out. Markov chain Monte Carlo techniques will be used to estimate the predictive distribution of the survival time, along with the posterior distribution of the regression parameters. The efficiency of computational meth- ods will also be compared, using both real and simulated data.
Original languageEnglish
Title of host publicationProceedings of the American Statistical Association
Pages1-8
Number of pages8
Publication statusPublished - 2006
Event2006 Joint Statistical Meetings - Seattle
Duration: 6 Aug 200610 Aug 2006

Conference

Conference2006 Joint Statistical Meetings
CitySeattle
Period6/8/0610/8/06

Keywords

  • AFT regression models, Bayesian semiparametrics, Mixture models, MCMC algorithms

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