Uncertainty quantification for monotone stochastic degradation models

Piao Chen, Zhi Sheng Ye*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

26 Citations (Scopus)
1 Downloads (Pure)


Degradation data are an important source of product reliability information. Two popular stochastic models for degradation data are the Gamma process and the inverse Gaussian (IG) process, both of which possess monotone degradation paths. Although these two models have been used in numerous applications, the existing interval estimation methods are either inaccurate given a moderate sample size of the degradation data or require a significant computation time when the size of the degradation data is large. To bridge this gap, this article develops a general framework of interval estimation for the Gamma and IG processes based on the method of generalized pivotal quantities. Extensive simulations are conducted to compare the proposed methods with existing methods under moderate and large sample sizes. Degradation data from capacitors are used to illustrate the proposed methods.

Original languageEnglish
Pages (from-to)207-219
JournalJournal of Quality Technology
Issue number2
Publication statusPublished - 2018
Externally publishedYes


  • Coverage probability
  • Gamma process
  • Generalized pivotal quantity
  • Inverse Gaussian process


Dive into the research topics of 'Uncertainty quantification for monotone stochastic degradation models'. Together they form a unique fingerprint.

Cite this