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 originale | English |
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Titolo della pubblicazione ospite | Proceedings of the American Statistical Association |
Pagine | 1-8 |
Numero di pagine | 8 |
Stato di pubblicazione | Pubblicato - 2006 |
Evento | 2006 Joint Statistical Meetings - Seattle Durata: 6 ago 2006 → 10 ago 2006 |
Convegno
Convegno | 2006 Joint Statistical Meetings |
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Città | Seattle |
Periodo | 6/8/06 → 10/8/06 |
Keywords
- AFT regression models, Bayesian semiparametrics, Mixture models, MCMC algorithms