title: A Bayesian semiparametric accelerated failure time model.
abstract: A Bayesian semiparametric approach is described for an accelerated failure time model. The error distribution is assigned a Pólya tree prior and the regression parameters a noninformative hierarchical prior. Two cases are considered: the first assumes error terms are exchangeable; the second assumes that error terms are partially exchangeable. A Markov chain Monte Carlo algorithm is described to obtain a predictive distribution for a future observation given both uncensored and censored data.
authors.affiliation: Department of Mathematics, Imperial College, London, UK.
authors.forename: S
authors.initials: S
authors.lastname: Walker
authors.forename: B K
authors.initials: BK
authors.lastname: Mallick
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001699:Biometry; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method; D009369:Neoplasms / Q000401:mortality; D016019:Survival Analysis