Accession Number : AD0775062

Title :   A Bayesian Approach to the Design and Analysis of Experiments for Regression Models,

Corporate Author : FRANK J SEILER RESEARCH LAB UNITED STATES AIR FORCE ACADEMY COLO

Personal Author(s) : Monaco,Salvatore J.

Report Date : JAN 1974

Pagination or Media Count : 119

Abstract : A Bayesian approach to the design and analysis of experiments for linear regression models is presented, where the objectives of the experiment are satisfied by a joint design criterion reflecting concern for both model discrimination and parameter estimation. Under the assumption of unknown variance, a probability mixture representing the state of the system is formulated and the procedure sequentially selects design points which maximize the posterior marginal variance of the response surface. Several stopping rules for termination of the experiment are proposed and a number of simulations illustrating the use of this procedure are included. Some advantages of this procedure are that it is easily implemented as an on-line controller and allows the experimenter maximum flexibility in allocating resources and deciding when to terminate experimentation. (Author)

Descriptors :   *Experimental design, *Bayes theorem, *Regression analysis, Decision theory, Estimates, Mathematical programming, Sampling, Sequential analysis

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE