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

Risultato della ricerca: Contributo in libroContributo a convegno

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteProceedings of the American Statistical Association
Pagine1-8
Numero di pagine8
Stato di pubblicazionePubblicato - 2006
Evento2006 Joint Statistical Meetings - Seattle
Durata: 6 ago 200610 ago 2006

Convegno

Convegno2006 Joint Statistical Meetings
CittàSeattle
Periodo6/8/0610/8/06

Keywords

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

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