Accession Number:

ADA043278

Title:

Nonparametric Bayes Estimation with Incomplete Dirichlet Prior Information.

Descriptive Note:

Interim rept.,

Corporate Author:

FLORIDA STATE UNIV TALLAHASSEE DEPT OF STATISTICS

Personal Author(s):

Report Date:

1977-06-01

Pagination or Media Count:

22.0

Abstract:

Typically, to use estimators which are Bayes with respect to Fergusons Dirichlet process prior, the statistician must provide a complete specification of the process parameter alpha, a non-negative non-null finite measure on a measureable space X, A. Here we take X R, the real line, and A B the Borel sigma-field. Mixed rules rules which minimize the average maximum risk are derived for estimating PrX or Y and for estimating rank order. These estimators are incomplete information analogues of Fergusons Bayes estimator of PrX or Y and the Campbell-Hollander Bayes estimator of rank order.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE