X-Gamma Lomax Distribution with Different Applications

Almetwally, Ehab M.; Kilai, Mutua; Aldallal, Ramy

doi:10.21608/jcese.2022.266566

Cited by 7 publications

(5 citation statements)

References 16 publications

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“…The GPA distribution is used in this section to model several real data examples from many scientific domains. Different distributions, including Weilbull, Lomax, XGamma Lomax (XGL) Almetwally et al [30], Inverse Weibul (IW), Inverted Nadarajah-Haghighi (INH), Tahir et al [31], and Akshaya distribution, are offered for comparison with the GPA distribution.…”

Section: Applicationmentioning

confidence: 99%

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Hamdy

Almetwally

2023

Computational Journal of Mathematical and Statistical Sciences

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Generalized power Akshaya distribution is a brand-new two-parameter distribution that builds on the Akshaya distribution first introduced by [1]. The lifetime data is intended to be modelled by this distribution. The generalised power Akshaya's parameters are estimated using both the non-Bayesian and Bayesian approaches in this work. The weighted least square estimation (WLSE), least square estimation (LSE), Cramer-von-Mises estimation (CVME), Anderson and Darling (AD) method of estimation, maximum product Space estimators (MPSE), and maximum likelihood estimation (MLE), six non-Bayesian estimation methods, are used to find the model parameters. The parameters of the suggested distribution were also determined using the squared error loss function and Bayesian estimating (BE) under independent gamma priors. The unknown parameters have been estimated using the Bayesian approach using Markov chain Monte Carlo (MCMC). Additionally, the parameters' average width of the confidence intervals and coverage probability are computed. Additionally, the reliable intervals for Bayesian estimates of the unknown parameters calculated.

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Section: Applicationmentioning

confidence: 99%

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Hamdy

Almetwally

2023

Computational Journal of Mathematical and Statistical Sciences

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“…For more information about simulation study see [10], [11], and [12]. Hence, we assess the performance of the MLE using correct initial values and an obvious algorithm as follows:…”

Section: Simulationmentioning

confidence: 99%

Study on an Extension to Lindley Distribution: Statistical Properties, Estimation and Simulation

Shafq

Helal

Elshaarawy

et al. 2022

Computational Journal of Mathematical and Statistical Sciences

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Recently, Nedjar and Zeghdoudi [6] proposed a new lifetime model called gamma Lindley distribution. Roozegar and Nadarajah [8] introduced some notes around gamma Lindley distribution including only some statistical properties and estimations depending on the same probability density function which proposed by Nedjar and Zeghdoudi [6]. In fact, the model proposed by Nedjar and Zeghdoudi [6] is not a probabilistic model. Further, some of its fundamental properties as well as parameter estimations are incorrect. Hence, all corrections which proposed by Roozegar and Nadarajah [8] are also incorrect. On the other hand, Messaadia and Zeghdoudi [5] proposed only one remark around the parameter space of gamma Lindley distribution which proposed by Nedjar and Zeghdoudi [6], but the mathematical properties and estimations are still wrong for both Nedjar and Zeghdoudi [6] and Roozegar and Nadarajah [8]. Because, Messaadia and Zeghdoudi [5] did not discuss any corrections around quantile function, entropies, estimation methods, simulation and data analysis. In this paper, several corrections including probability density function, quantile function, entropies, estimation the model parameters using the maximum likelihood estimation and moment estimation methods and simulation are discussed in-detail because the previous three papers not make a benefit to the readers, especially in practical field.

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“…In 2021, Mahmood et al [19] published an enlarged Cosine generalised family of distributions for dependability modelling: characteristics and applications with simulation analysis, and Muse et al [20] suggested a new flexible form of the loglogistic distribution. Citations [5], [17], and [6] in 2022 explored a family of produced distributions with applications.…”

Section: Introductionmentioning

confidence: 99%

Bayesian and Non-Bayesian Estimation Methods for Simulating the Parameter of the Akshaya Distribution

Tolba

2022

Computational Journal of Mathematical and Statistical Sciences

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For modelling lifetime data from biological research and engineering, the "Akshaya distribution" is a model one-parameter continuous distribution that was proposed by [15]. The non-Bayesian and Bayesian estimation methods for the Akshaya's parameter are also presented in this study. The weighted least square estimation (WLSE), least square estimation (LSE), Cramer-von-Mises estimation (CVME), and maximum likelihood estimation (MLE), five traditional estimation approaches, are used to find the model parameter. The parameter of the suggested distribution was also determined using the squared error loss function and Bayesian estimating (BE) under independent gamma priors. Finally, a simulation study is used to expound on the applicability and value of the proposed distribution.

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X-Gamma Lomax Distribution with Different Applications

Cited by 7 publications

References 16 publications

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Study on an Extension to Lindley Distribution: Statistical Properties, Estimation and Simulation

Bayesian and Non-Bayesian Estimation Methods for Simulating the Parameter of the Akshaya Distribution

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