Marshall–Olkin Alpha Power Lomax Distribution: Estimation Methods, Applications on Physics and Economics

Almongy, Hisham M.; Almetwally, Ehab M.; Mubarak, Amaal Elsayed

doi:10.18187/pjsor.v17i1.3402

Cited by 24 publications

(7 citation statements)

References 9 publications

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“…As a result, we employ the MCMC technique to approximate the value of integrals in equation ( 35). Many of studies used MCMC technique such as Al-Babtain et al [25], Tolba et al [26,27], and Bantan et al [28].…”

Section: Bayesian Estimationmentioning

confidence: 99%

[Retracted] Classical and Bayesian Inference of Marshall‐Olkin Extended Gompertz Makeham Model with Modeling of Physics Data

Mohamed

Al-Babtain

Elbatal

et al. 2022

Journal of Mathematics

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The purpose of this study is to present the Marshall- Olkin extended Gompertz Makeham MOEGM lifetime distribution, which has four parameters. As a result, we will describe some of the structural elements that are introduced for this model. The maximum likelihood approach is used to estimate the model parameters, and it is well known that likelihood estimators for unknown parameters are not always available. As a result, we examine the prior distributions, which allow for prior dependence among the components of the parameter vector, as well as the Bayesian estimators derived with respect to the squared error loss function. A Monte Carlo simulation research is carried out to examine the performance of the likelihood estimators and the Bayesian technique. Finally, we demonstrate the significance of the new model. And to conclude, we illustrate the importance of the new model by exploring some of the empirical applications of physics to show it’s flexibility and potentiality of a new model.

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Section: Bayesian Estimationmentioning

confidence: 99%

[Retracted] Classical and Bayesian Inference of Marshall‐Olkin Extended Gompertz Makeham Model with Modeling of Physics Data

Mohamed

Al-Babtain

Elbatal

et al. 2022

Journal of Mathematics

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“…In Table 6 , the TGL distribution is fitted to COVID-19 of France country. The TGL model is compared with other competitive models as Mead and Afify [ 16 ] proposed the Burr-XII model (KEBXII) with Kumaraswamy exponentiated, Weibull-Lomax (WL) distribution, Odds Exponential-Pareto IV (OEPIV) distribution proposed by Baharith et al [ 17 ], Marshall–Olkin Alpha power Weibull (MOAPW) by Almetwally et al [ 18 ], Marshall–Olkin Alpha power extended Weibull (MOAPEW) by Almetwally [ 19 ], Marshall–Olkin alpha power inverse Weibull (MOAPIW) by Basheer et al [ 20 ], Marshall–Olkin alpha power Lomax (MOAPL) by Almongy et al [ 21 ], and Gompertz Lomax (GOLOM) distribution by Oguntunde et al [ 11 ]. According to this result, we note that the estimate of TGL has the best measure where it has the smallest value of Cramer-von Mises ( W ∗ ), Anderson-Darling ( A ∗ ), and Kolmogorov- Smirnov (KS) statistic along with its P value.…”

Section: Real Data Analysismentioning

confidence: 99%

A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID‐19 Data

Azm

Almetwally

Alaziz

et al. 2021

Computational Intelligence and Neuroscience

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A new five-parameter transmuted generalization of the Lomax distribution (TGL) is introduced in this study which is more flexible than current distributions and has become the latest distribution theory trend. Transmuted generalization of Lomax distribution is the name given to the new model. This model includes some previously unknown distributions. The proposed distribution's structural features, closed forms for an rth moment and incomplete moments, quantile, and Rényi entropy, among other things, are deduced. Maximum likelihood estimate based on complete and Type-II censored data is used to derive the new distribution's parameter estimators. The percentile bootstrap and bootstrap-t confidence intervals for unknown parameters are introduced. Monte Carlo simulation research is discussed in order to estimate the characteristics of the proposed distribution using point and interval estimation. Other competitive models are compared to a novel TGL. The utility of the new model is demonstrated using two COVID-19 real-world data sets from France and the United Kingdom.

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“…To evaluate the estimation problem of the TI I HLOF − G family parameters, this part uses six estimate methods: maximum likelihood, least-square, a maximum product of spacing, weighted least square, Cram ér-von Mises, and Anderson-Darling. For more examples see [29][30][31][32][33].…”

Section: Estimation Methodsmentioning

confidence: 99%

“…The fourth data set is obtained from Ahmadini et al [36], it consists of 56 values of strength data measured in GPA, the single carbon fibers, and 1000 impregnated carbon fiber tows. The data are as follows: We compare the fit of the TI I HLOFW distribution with the following continuous lifetime distributions: Kumaraswamy Weibull (KW) by Cordeiro et al [37], Marshall-Olkin alpha power Weibull (MOAPW) by Almetwally [38], Marshall-Olkin alpha power inverse Weibull (MOAPIW) by Basheer et al [32], odd Perks Weibull (OPW) by Elbatal et al [14], Marshall-Olkin alpha power Lomax (MOAPL) by Almongy et al [33], and Odds exponential-Pareto IV (OWPIV) by Baharith et al [39].…”

Section: Strength Datamentioning

confidence: 99%

Type II Half-Logistic Odd Fréchet Class of Distributions: Statistical Theory and Applications

et al. 2022

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new class of statistical distributions called the Type II half-Logistic odd Fréchet-G class is proposed. The new class is a continuation of the unusual Fréchet class. This class is analytically feasible and could be used to evaluate real-world data effectively. The new suggested class of distributions has many new symmetrical and asymmetrical sub-models. We propose new four sub-models from the new class of distributions which are called Type II half-Logistic odd Fréchet exponential distribution, Type II half-Logistic odd Fréchet Rayleigh distribution, Type II half-Logistic odd FréchetWeibull distribution, and Type II half-Logistic odd Fréchet Lindley distribution. Some statistical features of Type II half-Logistic odd Fréchet-G class such as ordinary moments (ORMs), incomplete moments (INMs), moment generating function (MGEF), residual life (REL), and reversed residual life (RREL) functions, and Rényi entropy (RéE) are derived. Six methods of estimation such as maximum likelihood, least-square, a maximum product of spacing, weighted least square, Cram´ er-von Mises, and Anderson–Darling are produced to estimate the parameters. To test the six estimation methods’ performance, a simulation study is conducted. Four real-world data sets are utilized to highlight the importance and applicability of the proposed method.

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Marshall–Olkin Alpha Power Lomax Distribution: Estimation Methods, Applications on Physics and Economics

Cited by 24 publications

References 9 publications

[Retracted] Classical and Bayesian Inference of Marshall‐Olkin Extended Gompertz Makeham Model with Modeling of Physics Data

[Retracted] Classical and Bayesian Inference of Marshall‐Olkin Extended Gompertz Makeham Model with Modeling of Physics Data

A New Transmuted Generalized Lomax Distribution: Properties and Applications to COVID‐19 Data

Type II Half-Logistic Odd Fréchet Class of Distributions: Statistical Theory and Applications

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