A Dynamic General Linear Model for Inference From Accelerated Life Tests,

Abstract

We present a new approach for inference from accelerated life tests. Our approach is based on a dynamic general linear model setup which arises naturally from the accelerated life testing problem and uses linear Bayesian methods for inference. The advantage of the procedure is that it does not require large number of items to be tested and that it can deal with both censored and uncensored data. Furthermore, the approach produces closed form inference results. We illustrate the use of our approach with some actual accelerated life test data. (AN)

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Document Details

Document Type
Technical Report
Publication Date
Aug 31, 1987
Accession Number
ADA293952

Entities

People

  • Refik Soyer
  • Thomas A. Mazzuchi

Organizations

  • George Washington University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computers
  • Engineering
  • Environment
  • Equations
  • Failure Mode And Effect Analysis
  • Life Tests
  • Military Research
  • Models
  • Notation
  • Personal Computers
  • Probability
  • Reliability
  • Risk Analysis
  • Time Intervals
  • Uncertainty

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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