Accession Number : ADA332578

Title :   Bayes Sampling Designs for Selection Procedures

Descriptive Note : Technical rept.

Corporate Author : PURDUE UNIV LAFAYETTE IN DEPT OF STATISTICS

Personal Author(s) : Miescke, Klausche J.

PDF Url : ADA332578

Report Date : AUG 1997

Pagination or Media Count : 37

Abstract : From k independent populations P1,...,Pk, which belong to one parameter exponential family ?Ftheta!, theta in omega reflex subset contained in R, random samples of sizes m1,...,mk, respectively, are to be drawn. After the observations have been drawn, a selection procedure will be used to determine which of these k populations has the largest value of theta. Given a loss for selections at each parameter configuration, given n past observations, and given a prior for the k parameters, a Bayes selection procedure can be found and its Bayes risk can be determined, where both depend on m1,...,mk. Let the sample sizes be restricted by m1 + ... +mk = m, where m is fixed. The problem of how to find the optimum (minimum Bayes risk) sample design subject to this constraint is considered, as well as m-truncated sequential sampling allocations. Results for normal and binomial families, under the '0-1' loss and the linear loss, are presented and discussed. An introduction to Bayes selection procedures in included.

Descriptors :   *STATISTICAL SAMPLES, *SAMPLING, *BAYES THEOREM, RISK, PARAMETERS, POPULATION, CONFIGURATIONS, LOSSES, SELECTION.

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE