Statistical Properties of Weighted Shanker Distribution

Helal, Tamer S.; Elsehetry, Ahmed M.; Elshaarawy, Rasha S.

doi:10.21608/jcese.2022.269495

Cited by 5 publications

(4 citation statements)

References 14 publications

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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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“…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

View full text Add to dashboard Cite

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“…Oguntunde [8] presented the exponentiated weighted exponential distribution (EWED) and discussed its statistical properties. Helal et al [9] discussed statistical properties of weighted Shanker distribution. MLE and method of moments (MOM) were used for estimating the parameters of the model.…”

Section: Introductionmentioning

confidence: 99%

The Length-Biased Weighted Exponentiated Inverted Exponential Distribution: Properties and Estimation

El-Sherpieny

Abdel-Aziz

2023

Computational Journal of Mathematical and Statistical Sciences

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This paper introduces the length-biased weighted exponentiated inverted exponential distribution with sub models. The basic statistical properties are derived such as mean, mode, variance, reliability functions, moment generating function, characteristic function and order statistic distribution. Furthermore, maximum likelihood estimators of distribution parameters are obtained under censored Type I. Numerical study is applied. Variance-covariance matrix is obtained and Confidence intervals are calculated. Two real data sets are applied to compare performance of the proposed model with competitive distributions by using some criterions as measures of goodness fit.

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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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Statistical Properties of Weighted Shanker Distribution

Cited by 5 publications

References 14 publications

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

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

The Length-Biased Weighted Exponentiated Inverted Exponential Distribution: Properties and Estimation

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

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