The Power XLindley Distribution: Statistical Inference, Fuzzy Reliability, and COVID-19 Application

Bouhadjar, Meriem; Gemeay, Ahmed M.; Almetwally, Ehab M.; Zeghdoudi, Halim; Alshawarbeh, Etaf; Abdulrahman, Alanazi Talal; El‐Raouf, M. M. Abd; Hussam, Eslam

doi:10.1155/2022/9094078

Cited by 26 publications

(17 citation statements)

References 22 publications

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“…Mohamed et al [ 21 ] obtained fuzzy inference of reliability analysis for Type II Half logistic Weibull distribution. Meriem et al [ 22 ] derived statistical inference, and fuzzy reliability for power xlindley distribution. We can easily calculate the fuzzy dependability using the fuzzy probability formula, see Chen and Pham [ 23 ] where μ ( x ) is a membership function, For more reading see [ 24 ]…”

Section: Fuzzy Historymentioning

confidence: 99%

Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application

et al. 2023

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This paper provides a novel model that is more relevant than the well-known conventional distributions, which stand for the two-parameter distribution of the lifetime modified Kies Topp–Leone (MKTL) model. Compared to the current distributions, the most recent one gives an unusually varied collection of probability functions. The density and hazard rate functions exhibit features, demonstrating that the model is flexible to several kinds of data. Multiple statistical characteristics have been obtained. To estimate the parameters of the MKTL model, we employed various estimation techniques, including maximum likelihood estimators (MLEs) and the Bayesian estimation approach. We compared the traditional reliability function model to the fuzzy reliability function model within the reliability analysis framework. A complete Monte Carlo simulation analysis is conducted to determine the precision of these estimators. The suggested model outperforms competing models in real-world applications and may be chosen as an enhanced model for building a statistical model for the COVID-19 data and other data sets with similar features.

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Section: Fuzzy Historymentioning

confidence: 99%

Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application

et al. 2023

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“…Most nations’ adoption of “strict” health and safety safeguards has slowed down the rate at which COVID-19 spreads worldwide. Many researchers studied this pandemic such as Nagy et al [ 1 ], Hossam et al [ 2 ], Khan et al [ 3 ], Abu El Azm et al [ 4 ], Riad et al [ 5 ], Sindhu et al [ 6 ], Meriem et al [ 7 ], Hassan et al [ 8 ], Akgül et al [ 9 ], Alsuhabi et al [ 10 ], Almetwally [ 11 ], Caccavo [ 12 ] and Liu et al [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

General two-parameter distribution: Statistical properties, estimation, and application on COVID-19

et al. 2023

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In this paper, we introduced a novel general two-parameter statistical distribution which can be presented as a mix of both exponential and gamma distributions. Some statistical properties of the general model were derived mathematically. Many estimation methods studied the estimation of the proposed model parameters. A new statistical model was presented as a particular case of the general two-parameter model, which is used to study the performance of the different estimation methods with the randomly generated data sets. Finally, the COVID-19 data set was used to show the superiority of the particular case for fitting real-world data sets over other compared well-known models.

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“…Moreover, about the fuzzy reliability approach, Sabry et al [ 22 ] considered inference of fuzzy reliability model for inverse Rayleigh distribution. Also, Meriem et al [ 17 ] introducing the Power XLindley distribution studied statistical inference, fuzzy reliability and COVID-19 application.…”

Section: Introductionmentioning

confidence: 99%

Multi-component Reliability Inference in Modified Weibull Extension Distribution and Progressive Censoring Scheme

Kohansal

Pérez-González

Fernández

2023

Bull. Malays. Math. Sci. Soc.

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The statistical inference of multi-component reliability stress–strength system with nonidentical-component strengths is considered for the modified Weibull extension distribution in the presence of progressive censoring samples. For this aim, we study the estimation of multi-component reliability parameter in classical and Bayesian inference. So we derive some point and interval estimates such as maximum likelihood estimation, asymptotic confidence intervals, uniformly minimum variance unbiased estimation, approximate and exact Bayes estimation and highest posterior density intervals. Comparing of different estimates is provided by employing the Monte Carlo simulation, the mean squared error and coverage probabilities. Finally, one real data is utilized to illustrate the applicability of this new model.

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The Power XLindley Distribution: Statistical Inference, Fuzzy Reliability, and COVID-19 Application

Cited by 26 publications

References 22 publications

Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application

Data analysis for COVID-19 deaths using a novel statistical model: Simulation and fuzzy application

General two-parameter distribution: Statistical properties, estimation, and application on COVID-19

Multi-component Reliability Inference in Modified Weibull Extension Distribution and Progressive Censoring Scheme

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