Statistical Inference for Reliability from Stress Strength Relationships: The Normal Case.

Abstract

This paper examines statistical inference for P(Y < X), where X and Y are independent normal variates with unknown means and variances. The case of unequal variances is stressed. X can be interpreted as the strength of a component subjected to a stress Y, and P(Y < X) is the components reliability. For point estimation, a predictive estimator which can be calculated from the Behrens-Fisher distribution is derived and compared with the maximum likelihood and uniformly minimum variance unbiased estimators through a simulation study. Two approximate methods for obtaining confidence intervals and an approximate Bayesian probability interval is obtained. The actual coverage probabilities of these intervals is examined by simulation. (Author)

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1984
Accession Number
ADA142893

Entities

People

  • B. Reiser
  • I. Guttman

Organizations

  • University of Wisconsin–Madison

Tags

DTIC Thesaurus Topics

  • Bayesian Networks
  • Computational Science
  • Confidence Limits
  • Data Science
  • Estimators
  • Information Science
  • Mathematics
  • Military Research
  • Probability
  • Random Variables
  • Reliability
  • Sampling
  • Simulations
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Structural Dynamics.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference