Different Estimation Methods for Type I Half-Logistic Topp–Leone Distribution

ZeinEldin, Ramadan A.; Chesneau, Christophe; Jamal, Farrukh; Elgarhy, Mohammed

doi:10.3390/math7100985

Cited by 14 publications

(7 citation statements)

References 32 publications

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“…The requirement of obtaining suitable models and statistical distributions has become essential, since defining new distributions will enable us to better describe and predict phenomenal and experimental data. See for example [1][2][3][4][5][6][7][8][9], among others.…”

Section: Introductionmentioning

confidence: 99%

A Flexible Extension to an Extreme Distribution

et al. 2021

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The aim of this paper is not only to propose a new extreme distribution, but also to show that the new extreme model can be used as an alternative to well-known distributions in the literature to model various kinds of datasets in different fields. Several of its statistical properties are explored. It is found that the new extreme model can be utilized for modeling both asymmetric and symmetric datasets, which suffer from over- and under-dispersed phenomena. Moreover, the hazard rate function can be constant, increasing, increasing–constant, or unimodal shaped. The maximum likelihood method is used to estimate the model parameters based on complete and censored samples. Finally, a significant amount of simulations was conducted along with real data applications to illustrate the use of the new extreme distribution.

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Section: Introductionmentioning

confidence: 99%

A Flexible Extension to an Extreme Distribution

et al. 2021

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“…Recent studies have also pointed out these facts in other practical settings. See, for instance, References [14][15][16] which consider the generalized Weibull, Topp-Leone and power Lomax distributions as parental distributions, respectively. However, the half logistic generated family remains underexploited and, based on the previous promising studies, needs more thorough attention.…”

Section: Introductionmentioning

confidence: 99%

Half Logistic Inverse Lomax Distribution with Applications

et al. 2021

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The last years have revealed the importance of the inverse Lomax distribution in the understanding of lifetime heavy-tailed phenomena. However, the inverse Lomax modeling capabilities have certain limits that researchers aim to overcome. These limits include a certain stiffness in the modulation of the peak and tail properties of the related probability density function. In this paper, a solution is given by using the functionalities of the half logistic family. We introduce a new three-parameter extended inverse Lomax distribution called the half logistic inverse Lomax distribution. We highlight its superiority over the inverse Lomax distribution through various theoretical and practical approaches. The derived properties include the stochastic orders, quantiles, moments, incomplete moments, entropy (Rényi and q) and order statistics. Then, an emphasis is put on the corresponding parametric model. The parameters estimation is performed by six well-established methods. Numerical results are presented to compare the performance of the obtained estimates. Also, a simulation study on the estimation of the Rényi entropy is proposed. Finally, we consider three practical data sets, one containing environmental data, another dealing with engineering data and the last containing insurance data, to show how the practitioner can take advantage of the new half logistic inverse Lomax model.

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“…The MADE has a high performance in the case of many location scale distributions and offers a better solution than MLE in special estimation problems which can occur in modelling complex phenomena (Raschke, 2017). Additionally, the MADE was used in some recent researches by Dey et al (2017; and ZeinEldin (2019a;2019b) The rest of this paper is organized as following, firstly, background of the MOPLx distribution including its pdf, the cumulative distribution function (cdf), some essential mathematical properties and parameter estimation using MLE are studied. Secondly, MADE for the MOPLx distribution is discussed and its Anderson-Darling distance function is also derived.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation methods for the Marshall-Olkin Power Lomax distribution and its applications

Sudsuk¹,

Yamrubboon²

2021

j.appsci

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The Marshall-Olkin Power Lomax (MOPLx) distribution, being extended the Power Lomax distribution, is obtained by using Marshall-Olkin (MO) scheme. This distribution can be applied for skewed data set and used as an alternative to Gamma or Weibull distributions. The main objective of this paper is to compare common method, maximum likelihood estimator (MLE) with the minimum Anderson-Darling estimator (MADE), for the shape and scale parameters of the MOPLx distribution. The efficiency of both estimators for MOPLx distribution is examined via a simulation study with different sample sizes and its specified parameters by using bias, standard deviation and root mean square error as the criteria for the comparison between MLE and MADE. Furthermore, for application of the MOPLx distribution, four life time data sets are exemplified to evaluate the performances of these estimators in both small and large samples.

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Different Estimation Methods for Type I Half-Logistic Topp–Leone Distribution

Cited by 14 publications

References 32 publications

A Flexible Extension to an Extreme Distribution

A Flexible Extension to an Extreme Distribution

Half Logistic Inverse Lomax Distribution with Applications

Parameter estimation methods for the Marshall-Olkin Power Lomax distribution and its applications

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