Journal
TECHNOMETRICS
Volume 56, Issue 4, Pages 504-513Publisher
AMER STATISTICAL ASSOC
DOI: 10.1080/00401706.2013.869261
Keywords
Score test; Random effects; EM algorithm; Degradation data
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Funding
- City University of Hong Kong [9380058]
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This article investigates the semiparametric inference of the simple Gamma-process model and a random-effects variant. Maximum likelihood estimates of the parameters are obtained through the EM algorithm. The bootstrap is used to construct confidence intervals. A simulation study reveals that an estimation based on the full likelihood method is more efficient than the pseudo likelihood method. In addition, a score test is developed to examine the existence of random effects under the semiparametric scenario. A comparison study using a fatigue-crack growth dataset shows that performance of a semiparametric estimation is comparable to the parametric counterpart. This article has supplementary material online.
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