Bayesian semiparametric inference for the accelerated failuretime model Academic Article

abstract

• AbstractBayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixtureofDirichletprocesses prior on the unknown error distribution. A Markovchain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.

authors

• Mallick, Bani

published proceedings

• Canadian Journal of Statistics

author list (cited authors)

• Kuo, L., & Mallick, B.

citation count

• 90

complete list of authors

• Kuo, Lynn||Mallick, Bani

publication date

• December 1997

publisher

• Wiley  Publisher

published in

• Canadian Journal of Statistics  Journal

keywords

• 49 Mathematical Sciences
• 4905 Statistics

Digital Object Identifier (DOI)

• 10.2307/3315341

start page

• 457

end page

• 472

volume

• 25

issue

• 4

URL

• http://dx.doi.org/10.2307/3315341