Classical and Bayesian Inference of Conditional Stress-Strength Model under Kumaraswamy Distribution | Zendy

Fathy H. Riad | Zendy; Mohammad Mehdi Saber | Zendy; Mehrdad Taghipour | Zendy; M. M. Abd ElRaouf | Zendy

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Classical and Bayesian Inference of Conditional Stress-Strength Model under Kumaraswamy Distribution

Author(s) -

Fathy H. Riad,

Mohammad Mehdi Saber,

Mehrdad Taghipour,

M. M. Abd ElRaouf

Publication year - 2021

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2021/1087871

Subject(s) - mathematics , estimator , statistics , bayes estimator , conditional probability distribution , inference , confidence interval , extension (predicate logic) , computer science , artificial intelligence , programming language

Stress-strength models have been frequently studied in recent years. An applicable extension of these models is conditional stress-strength models. The maximum likelihood estimator of conditional stress-strength models, asymptotic distribution of this estimator, and its confidence intervals are obtained for Kumaraswamy distribution. In addition, Bayesian estimation and bootstrap method are applied to the model.

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