Predictive analyses for nonhomogeneous Poisson processes with power law using Bayesian approach

Jun Wu Yu, Guo Liang Tian*, Man Lai TANG

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

Nonhomogeneous Poisson process (NHPP) also known as Weibull process with power law, has been widely used in modeling hardware reliability growth and detecting software failures. Although statistical inferences on the Weibull process have been studied extensively by various authors, relevant discussions on predictive analysis are scattered in the literature. It is well known that the predictive analysis is very useful for determining when to terminate the development testing process. This paper presents some results about predictive analyses for Weibull processes. Motivated by the demand on developing complex high-cost and high-reliability systems (e.g., weapon systems, aircraft generators, jet engines), we address several issues in single-sample and two-sample prediction associated closely with development testing program. Bayesian approaches based on noninformative prior are adopted to develop explicit solutions to these problems. We will apply our methodologies to two real examples from a radar system development and an electronics system development.

Original languageEnglish
Pages (from-to)4254-4268
Number of pages15
JournalComputational Statistics and Data Analysis
Volume51
Issue number9
DOIs
Publication statusPublished - 15 May 2007

Scopus Subject Areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

User-Defined Keywords

  • Bayesian approach
  • Nonhomogeneous Poisson process
  • Noninformative prior
  • Prediction intervals
  • Reliability growth

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