Prediction of Kumaraswamy distribution in constant‐stress model based on type‐I hybrid censored data

Fawzy, Mohamad A.

doi:10.1002/sam.11452

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Currently, many scholars have conducted research on the parameter estimation of the Ku lifetime distribution. Fawzy (2020) considered the prediction of constant-stress accelerated life tests for products following the Ku distribution under Type-I hybrid CeSc. Mohan and Chacko (2021) investigated different estimation methods of Kumaraswamy-exponential distribution based on adaptive type-II progressive CeSc.…”

Section: Introductionmentioning

confidence: 99%

Statistical inference for marshall-olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censored dependent competing risks data

Haghverdi Vardani,

Panahi,

Behzadi

2024

Phys. Scr.

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The statistical inference under competing risks model is of great significance in reliability analysis and it is more practical to assume that they have dependent competing causes of failure in actual situations. In this article, we make inference for unknown parameters of a Marshall-Olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censoring mechanism. The maximum likelihood estimations of the unknown parameters are derived, and the Fisher information matrix is then employed to construct asymptotic confidence intervals. Bayes estimates are evaluated against squared error and linex loss functions assuming ordered Gamma-Dirichlet and Gamma-Dirichlet prior distributions for order restriction and without order restriction cases respectively. The Metropolis-Hasting and Lindley techniques are applied to acquire the estimates of all unknown parameters. A thorough simulation analysis is demonstrated to assess the performance of the supplied approaches across various sample sizes. The usefulness of the techniques is illustrated using real engineering data to prove their versatility in practical applications.

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Section: Introductionmentioning

confidence: 99%

Statistical inference for marshall-olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censored dependent competing risks data

Haghverdi Vardani,

Panahi,

Behzadi

2024

Phys. Scr.

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“…Golizadeh et al [42] used ungrouped data to analyze classical and Bayesian estimators for the shape parameter of the KuD and also considered the relationship between them. For more details, see Sindhu et al [43], Sharaf EL-Deen et al [44], Wang [45], Kumar et al [46] and Fawzy [47].…”

Section: Introductionmentioning

confidence: 99%

Multi Stress-Strength Reliability Based on Progressive First Failure for Kumaraswamy Model: Bayesian and Non-Bayesian Estimation

Yousef

Almetwally

2021

Symmetry

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It is highly common in many real-life settings for systems to fail to perform in their harsh operating environments. When systems reach their lower, upper, or both extreme operating conditions, they frequently fail to perform their intended duties, which receives little attention from researchers. The purpose of this article is to derive inference for multi reliability where stress-strength variables follow unit Kumaraswamy distributions based on the progressive first failure. Therefore, this article deals with the problem of estimating the stress-strength function, R when X,Y, and Z come from three independent Kumaraswamy distributions. The classical methods namely maximum likelihood for point estimation and asymptotic, boot-p and boot-t methods are also discussed for interval estimation and Bayes methods are proposed based on progressive first-failure censored data. Lindly’s approximation form and MCMC technique are used to compute the Bayes estimate of R under symmetric and asymmetric loss functions. We derive standard Bayes estimators of reliability for multi stress–strength Kumaraswamy distribution based on progressive first-failure censored samples by using balanced and unbalanced loss functions. Different confidence intervals are obtained. The performance of the different proposed estimators is evaluated and compared by Monte Carlo simulations and application examples of real data.

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Statistical inference for the step-stress model with competing risks from the Kumaraswamy distribution under progressive type-II censoring

Wang,

Ye,

Gui

2023

Communications in Statistics - Theory and Methods

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Prediction of Kumaraswamy distribution in constant‐stress model based on type‐I hybrid censored data

Cited by 3 publications

References 19 publications

Statistical inference for marshall-olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censored dependent competing risks data

Statistical inference for marshall-olkin bivariate Kumaraswamy distribution under adaptive progressive hybrid censored dependent competing risks data

Multi Stress-Strength Reliability Based on Progressive First Failure for Kumaraswamy Model: Bayesian and Non-Bayesian Estimation

Statistical inference for the step-stress model with competing risks from the Kumaraswamy distribution under progressive type-II censoring

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