Journal ArticleDOI
Bayesian semiparametric inference for the accelerated failure‐time model
Lynn Kuo,Bani K. Mallick +1 more
TLDR
In this paper, a Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution of a log-linear model, and a model selection method for obtaining a more parsimonious set of predictors is studied.Abstract:
Bayesian 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 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.read more
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References
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Journal ArticleDOI
Nonparametric Bayesian analysis of the accelerated failure time model
TL;DR: In this article, a fully Bayesian analysis for the estimation of regression coefficients and survival curves was developed for the accelerated failure time with a Dirichlet process, and the practical difficulties associated with this theoretically desirable analysis were emphasized.
Journal ArticleDOI
Testing for dispersive ordering
TL;DR: In this paper, an asymptotically distribution-free test is proposed and studied for testing the null hypothesis H0: Fdisp = G versus H1: FDisp < G, that is G−1(β)−G−1 (α)⩾F−1β) −F− 1(α) for 0⩽α<β⩻1.
Journal ArticleDOI
A note on Bayes empirical Bayes estimation by means of Dirichlet processes
TL;DR: In this paper, the Dirichlet process hyperprior approach for general empirical Bayes problems is used to derive estimators for any sample size, expressed concisely as ratios of two multidimensional integrals.