Accelerated Life Tests That Maximize Shannon Information,

Abstract

The current literature on accelerated life tests emphasizes issues of inference and extrapolation under a given design, rather than the optimal design of an accelerated test. In this expository paper, we outline a framework for a coherent approach for the conduct of accelerated tests. Our approach is based on the Bayesian paradigm for experimental designs under linear models and involves the specification of a utility function. For the latter we adopt Shannon Information between predictive densities. The optimal design is then selected via the principle of maximum expected utility. We illustrate the approach with some special cases. (MM)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1988
Accession Number
ADA293944

Entities

People

  • Isabella Verdinelli
  • Nick Polson
  • Nozer Singpurwalla

Organizations

  • George Washington University

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Accelerated Testing
  • Ball Bearings
  • Bayesian Inference
  • Clinical Trials
  • Data Science
  • Distribution Functions
  • Experimental Design
  • Information Science
  • Life Tests
  • Military Research
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistics
  • Stress Tests
  • Test Methods

Readers

  • Software Engineering
  • Software Engineering.
  • Statistical inference.

Technology Areas

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