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Bayesian Semiparametric Inference for the Accelerated Failure Time Model
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kuo, Lynn |
| Copyright Year | 1997 |
| Abstract | Bayesian semi-parametric inference is considered for a log-linear model. This model consists of a parametric component for the regression coeecients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coeecients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov chain 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. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://merlot.stat.uconn.edu/~lynn/publication/tr9537.ps |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Bayesian network Censor Censoring (statistics) Inference Leucaena pulverulenta Linear model Log-linear model Markov chain Monte Carlo Model selection Monte Carlo method Occam's razor Selection (genetic algorithm) Semiconductor industry Semiparametric model Subgroup |
| Content Type | Text |
| Resource Type | Article |
