Accession Number : ADP007223
Title : Sampling Based Approach to Computing Nonparametric Bayesian Estimators with Doubly Censored Data,
Corporate Author : CONNECTICUT UNIV STORRS DEPT OF STATISTICS
Personal Author(s) : Kuo, Lynn
Report Date : 1992
Pagination or Media Count : 4
Abstract : 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.
Descriptors : *STATISTICS, APPROACH, INTERVALS, MIXTURES, NUMBERS, PAPER, SAMPLERS, SAMPLING.
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE