Sampling Based Approach to Computing Nonparametric Bayesian Estimators with Doubly Censored Data,
CONNECTICUT UNIV STORRS DEPT OF STATISTICS
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Nonparametric Bayesian estimators with Dirichlet process priors for doubly censored data can be derived from mixtures of Dirichlet distributions. To circumvent the computational difficulties in evaluating these mixtures, this paper describes the Gibbs sampling approach to approximating them. The Gibbs samplers augment the censored data by the number of observations falling into each interval. An example taken from Turnbull 1974 is given to illustrate the roach. Gibbs sampling Stochastic substitution Dirichlet process priors Doubly censored data.
- Statistics and Probability