Statistical inference and prediction for the Weibull process with incomplete observations

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

In this article, statistical inference and prediction analyses for the Weibull process with incomplete observations via classical approach are studied. Specifically, observations in the early developmental phase of a testing program cannot be observed. We derive the closed-form expressions for the maximum likelihood estimates of the parameters in both the failure- and time-truncated Weibull processes. Confidence interval and hypothesis testing for the parameters of interest are considered. In addition, predictive inferences on future failures and the goodness-of-fit test of the model are developed. Two real examples from an engine system development study and a Boeing air-conditioning system development study are presented to illustrate the proposed methodologies.

Original languageEnglish
Pages (from-to)1587-1603
Number of pages17
JournalComputational Statistics and Data Analysis
Volume52
Issue number3
DOIs
Publication statusPublished - 1 Jan 2008

Scopus Subject Areas

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

User-Defined Keywords

  • AMSAA model
  • Confidence intervals
  • Goodness-of-fit test
  • Nonhomogeneous Poisson process
  • Prediction limits
  • Reliability growth
  • Weibull process

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