The Fisher Information Function and Design of Experiments for Estimation in Non-Linear Statistical Models.

Abstract

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)

Document Details

Document Type
Technical Report
Publication Date
Jan 15, 1973
Accession Number
AD0755148

Entities

People

  • Shelemyahu Zacks

Organizations

  • Case Western Reserve University

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Data Science
  • Efficiency
  • Experimental Design
  • Information Science
  • Models
  • Nonlinear Dynamics
  • Reliability

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Regression Analysis.
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms