The Marshall-Olkin Exponential Weibull Distribution

Saboor, Abdus; Provost, Serge B.; Pogány, Tibor K.

doi:10.15672/hjms.2015478614

Cited by 12 publications

(10 citation statements)

References 22 publications

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“…In this regard, we aim to compare the fits of the REW model with those of five other solid models, also defined with the Weibull distribution as baseline, namely: transmuted-Weibull (TW) model studied by [22], Marshall-Olkin exponential Weibull (MOEW) model introduced by [23], odd log-logistic modified-Weibull (OLLMW) model proposed by [24], Kumaraswamy-Weibull (KW) model introduced by [15] and Beta-Weibull (BW) model proposed by [25].…”

Section: Data Fittingmentioning

confidence: 99%

On a New Result on the Ratio Exponentiated General Family of Distributions with Applications

Bantan

Jamal²,

Chesneau

et al. 2020

Mathematics

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In this paper, we first show a new probability result which can be concisely formulated as follows: the function 2 G β / ( 1 + G α ) , where G denotes a baseline cumulative distribution function of a continuous distribution, can have the properties of a cumulative distribution function beyond the standard assumptions on α and β (possibly different and negative, among others). Then, we provide a complete mathematical treatment of the corresponding family of distributions, called the ratio exponentiated general family. To link it with the existing literature, it constitutes a natural extension of the type II half logistic-G family or, from another point of view, a compromise between the so-called exponentiated-G and Marshall-Olkin-G families. We show that it possesses tractable probability functions, desirable stochastic ordering properties and simple analytical expressions for the moments, among others. Also, it reaches high levels of flexibility in a wide statistical sense, mainly thanks to the wide ranges of possible values for α and β and thus, can be used quite effectively for the real data analysis. We illustrate this last point by considering the Weibull distribution as baseline and three practical data sets, with estimation of the model parameters by the maximum likelihood method.

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Section: Data Fittingmentioning

confidence: 99%

On a New Result on the Ratio Exponentiated General Family of Distributions with Applications

Bantan

Jamal²,

Chesneau

et al. 2020

Mathematics

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“…Based on the above MO transformation several researchers have considered various extended distributions in the last few years. For instance, Marshall and Olkin (1997) developed the MO exponential and MO Weibull distributions, Rao et al (2009) proposed the MO extended Lomax distribution, Krishna et al (2013) proposed the MO Fréchet distribution, MO exponential Weibull distribution has been discussed by Pogány et al (2015), MO power Lomax distribution has been introduced by Haq et al (2020) and generalized MO inverse Lindley by Bantan et al (2020) among others.…”

Section: Introductionmentioning

confidence: 99%

Parameter estimation of an extended inverse power Lomax distribution with Type I right censored data

Hassan

Nassr

2021

CSAM

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In this paper, we introduce an extended form of the inverse power Lomax model via Marshall-Olkin approach. We call it the Marshall-Olkin inverse power Lomax (MOIPL) distribution. The four-parameter MOIPL distribution is very flexible which contains some former and new models. Vital properties of the MOIPL distribution are affirmed. Maximum likelihood estimators and approximate confidence intervals are considered under Type I censored samples. Maximum likelihood estimates are evaluated according to simulation study. Bayesian estimators as well as Bayesian credible intervals under symmetric loss function are obtained via Markov chain Monte Carlo (MCMC) approach. Finally, the flexibility of the new model is analyzed by means of two real data sets. It is found that the MOIPL model provides closer fits than some other models based on the selected criteria.

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“…Thus, to account for this disadvantage, some exponential distribution generalizations for modeling lifetime data have been recently proposed [2][3]. In recent years many exponential distribution generalizations have been developed, such as the Marshall Olkin length biased exponential distribution [4], exponentiated exponential [5][6], generalized exponentiated moment exponential [7], extended exponentiated exponential [8], Marshall-Olkin exponential Weibull [9], Marshall-Olkin generalized exponential [3], and exponentiated moment exponential [10] distributions.…”

Section: Introductionmentioning

confidence: 99%

New Generalization of Length Biased Exponential Distribution with Applications

Maxwell¹,

Oyamakin²,

Chukwu³

et al. 2019

JAAM

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In this paper, a compound continuous distribution (i.e. Exponentiated length biased exponential (E-LBE) distribution) is given. Also, the statistical properties of the proposed distribution are examined, and the parameters are obtained by maximum likelihood estimation method. The flexibility, adequacy, and superiority of the proposed model were investigated by means of applications to dataset. The result indicates that the E-LBE distribution outperformed the competing distributions considered.

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The Marshall-Olkin Exponential Weibull Distribution

Cited by 12 publications

References 22 publications

On a New Result on the Ratio Exponentiated General Family of Distributions with Applications

On a New Result on the Ratio Exponentiated General Family of Distributions with Applications

Parameter estimation of an extended inverse power Lomax distribution with Type I right censored data

New Generalization of Length Biased Exponential Distribution with Applications

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