Accession Number:

ADP007223

Title:

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

Descriptive Note:

Corporate Author:

CONNECTICUT UNIV STORRS DEPT OF STATISTICS

Personal Author(s):

• Kuo, Lynn

Report Date:

1992-01-01

Pagination or Media Count:

4.0

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