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

Fathy H Riad; Mohammad Mehdi Saber; Mehrdad Taghipour; M M Abd El-Raouf

doi:10.1155/2021/1087871

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

Comput Intell Neurosci. 2021 Jul 30:2021:1087871. doi: 10.1155/2021/1087871. eCollection 2021.

Authors

Fathy H Riad^{1

2}, Mohammad Mehdi Saber³, Mehrdad Taghipour⁴, M M Abd El-Raouf⁵

Affiliations

¹ Department of Mathematics, College of Science, Jouf University, Sakaka, Saudi Arabia.
² Department of Mathematics, Faculty of Science, Minia University, Minia, Egypt.
³ Department of Statistics, Higher Education Center of Eghlid, Eghlid, Iran.
⁴ Department of Statistics, Faculty of Sciences, University of Qom, Qom, Iran.
⁵ Basic and Applied Science Institute, Arab Academy for Science, Technology and Maritime Transport (AASTMT), Alexandia, Egypt.

Abstract

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.

MeSH terms

Bayes Theorem
Likelihood Functions
Models, Statistical*