The Fisher Information Function and Design of Experiments for Estimation in Non-Linear Statistical Models.
CASE WESTERN RESERVE UNIV CLEVELAND OHIO DEPT OF MATHEMATICS AND STATISTICS
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The problem of designing experiments for estimating parameters of nonlinear models is studied in a Bayesian framework, with the objective of maximizing the anticipated Fisher information. The theoretical set-up for optimal two-stage designs is formulated. Optimal designs for reliability attribute life testing experiments are derived. A non-Bayesian measure of efficiency of the designs is defined and computed. Sequential group testing experiments which are epsilon-most efficient are presented. Author
- Statistics and Probability