Premium
A Bayesian Semiparametric Accelerated Failure Time Model
Author(s) -
Walker Stephen,
Mallick Bani K.
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00477.x
Subject(s) - markov chain monte carlo , bayesian probability , accelerated failure time model , semiparametric regression , statistics , mathematics , computer science , semiparametric model , markov chain , econometrics , regression , proportional hazards model , parametric statistics
Summary. A Bayesian semiparametric approach is described for an accelerated failure time model. The error distribution is assigned a Polya 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.