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Parametric inference for multiple repairable systems under dependent competing risks
Author(s) -
Somboonsavatdee Anupap,
Sen Ananda
Publication year - 2014
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2079
Subject(s) - warranty , inference , parametric statistics , computer science , statistical inference , sample (material) , event (particle physics) , econometrics , focus (optics) , reliability engineering , parametric model , process (computing) , power (physics) , operations research , artificial intelligence , economics , engineering , statistics , mathematics , law , chemistry , physics , optics , chromatography , quantum mechanics , political science , operating system
The focus of this article is on the analysis of repairable systems that are subject to multiple sources of recurrence. The event of interest at the system level is assumed to be caused by the earliest occurrence of a source, thereby conforming to a series system competing risks framework. Parametric inference is carried out under the power law process model that has found significant attention in industrial applications. Dependence among the cause‐specific recurrent processes is induced via a shared frailty structure. The theoretical inference results are implemented to a warranty database for a fleet of automobiles , for which the warranty repair is triggered by the failure of one of many components. Extensive finite‐sample simulation is carried out to supplement the asymptotic findings. Copyright © 2014 John Wiley & Sons, Ltd.