Semiparametric Estimation of Gamma Processes for Deteriorating Products

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 est...

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Bibliographic Details
Published in:Technometrics 2014-10, Vol.56 (4), p.504-513
Main Authors: Ye, Zhi-Sheng, Xie, Min, Tang, Loon-Ching, Chen, Nan
Format: Article
Language:English
Subjects:
Comparative analysis
Datasets
Degradation data
EM algorithm
Estimating techniques
Estimation bias
Estimation methods
Maximum likelihood estimation
Maximum likelihood method
Missing data
Nonparametric models
Parameter estimation
Parametric models
Random effects
Score test
Semiparametric modeling
Shape functions
Simulation
Simulations
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Summary: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.
ISSN:0040-1706
1537-2723
DOI:10.1080/00401706.2013.869261