Robust estimation of dependent competing risk model under interval monitoring and determining optimal inspection intervals

Recently, a growing interest is evident in modelling dependent competing risks in lifetime prognosis problems. In this work, we propose to model the dependent competing risks by Marshal‐Olkin bivariate exponential distribution. The observable data consists of a number of failures due to different ca...

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Vydané v:Applied stochastic models in business and industry Ročník 40; číslo 4; s. 926 - 944
Hlavní autori: Baghel, Shanya, Mondal, Shuvashree
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bognor Regis Wiley Subscription Services, Inc 01.07.2024
Predmet:
Bivariate analysis
competing risk
Design optimization
divergence based robust estimation
Failure times
Genetic algorithms
Heuristic methods
influence function
Influence functions
Inspection
Intervals
Marshal‐Olkin bivariate exponential distribution
Multiple objective analysis
multi‐objective optimization
Probability distribution functions
Robustness
Service life assessment
Sorting algorithms
ISSN:1524-1904, 1526-4025
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Shrnutí:Recently, a growing interest is evident in modelling dependent competing risks in lifetime prognosis problems. In this work, we propose to model the dependent competing risks by Marshal‐Olkin bivariate exponential distribution. The observable data consists of a number of failures due to different causes across different time intervals. The failure count data is common in instances like one‐shot devices where the state of the subjects is inspected at different inspection times rather than the exact failure times. The point estimation of the lifetime distribution in the presence of competing risk has been studied through a divergence‐based robust estimation method called minimum density power divergence estimation (MDPDE) with and without constraint. The optimal value of the tuning parameter has been obtained. The testing of the hypothesis is performed based on a Wald‐type test statistic. The influence function is derived for the point estimator and the test statistic, reflecting the degree of robustness. Another key contribution of this work is determining the optimal inspection times based on predefined objectives. This article presents the determination of multi‐criteria‐based optimal design. Population‐based heuristic algorithm nondominated sorting‐based multiobjective Genetic algorithm is exploited to solve this optimization problem.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1524-1904
1526-4025
DOI:10.1002/asmb.2854