Accession Number:

ADP007223

Title:

Sampling Based Approach to Computing Nonparametric Bayesian Estimators with Doubly Censored Data,

Personal Author(s):

Corporate Author:

CONNECTICUT UNIV STORRS DEPT OF STATISTICS

Report Date:

1992-01-01

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.

Supplementary Note:

This article is from 'Computing Science and Statistics: Proceedings of the Symposium on Interface Critical Applications of Scientific Computing (23rd): Biology, Engineering, Medicine, Speech Held in Seattle, Washington on 21-24 April 1991,' AD-A252 938, p612-615.

Pages:

0004

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0.00MB

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