Type I General Exponential Class of distributions

Hamedani, G. G.; Yousof, Haitham M.; Rasekhi, Mahdi; Alizadeh, Morad; Najibi, Seyed Morteza

doi:10.18187/pjsor.v14i1.2193

Cited by 55 publications

(23 citation statements)

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“…The * and * statistics are given by: * = (1 + 1/2 ) 1/(12 ) + , and: * = ( ) + , We compared the fits of the MOBE-2 distribution with some competitive models, namely: exponential (E (β)), odd Lindley exponential (OLiE), MO exponential (MOE (α, β)), moment exponential (MomE (β)), the logarithmic Burr-Hatke exponential (Log BrHE (β)), generalized MO exponential (GMOE (α, α, β)), beta exponential (BE (a, b, β)), MO-Kumaraswamy exponential (MOKwE (α, a, b, β)), Kumaraswamy exponential (KwE (a, b, β)), and Kumaraswamy MO exponential (KwMOE (α, a, b, β)). See the PDFs of the competitive moels in [21][22][23][24][25][26][27][28][29][30][31]. We considered the Cramér-Von Mises (W * ), the Anderson-Darling (A * ), and the Kolmogorov-Smirnov (KS) statistics.…”

Section: Data Imentioning

confidence: 99%

A New Flexible Three-Parameter Model: Properties, Clayton Copula, and Modeling Real Data

2020

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In this article, we introduced a new extension of the binomial-exponential 2 distribution. We discussed some of its structural mathematical properties. A simple type Copula-based construction is also presented to construct the bivariate- and multivariate-type distributions. We estimated the model parameters via the maximum likelihood method. Finally, we illustrated the importance of the new model by the study of two real data applications to show the flexibility and potentiality of the new model in modeling skewed and symmetric data sets.

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Section: Data Imentioning

confidence: 99%

A New Flexible Three-Parameter Model: Properties, Clayton Copula, and Modeling Real Data

2020

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“…This subsection deals with the characterizations of BrXEW distribution based on the ratio of two truncated moments. Our first characterization employs a theorem due to Glänzel (1987), see Theorem 1 (see Hamedani et al (2018) and Hamedani et al (2019)). The result, however, holds also when the interval is not closed, since the condition of the Theorem is on the interior of .…”

Section: Characterizations Based On Two Truncated Momentsmentioning

confidence: 99%

The Burr X Exponentiated Weibull Model: Characterizations,Mathematical Properties and Applications to Failure and Survival Times Data

Khalil

Hamedani

Yousof

2019

Pak.j.stat.oper.res.

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In this article, we introduce a new three-parameter lifetime model called the Burr X exponentiated Weibull model. The major justification for the practicality of the new lifetime model is based on the wider use of the exponentiated Weibull and Weibull models. We are motivated to propose this new lifetime model because it exhibits increasing, decreasing, bathtub, J shaped and constant hazard rates. The new lifetime model can be viewed as a mixture of the exponentiated Weibull distribution. It can also be viewed as a suitable model for fitting the right skewed, symmetric, left skewed and unimodal data. We provide a comprehensive account of some of its statistical properties. Some useful characterization results are presented. The maximum likelihood method is used to estimate the model parameters. We prove empirically the importance and flexibility of the new model in modeling two types of lifetime data. The proposed model is a better fit than the Poisson Topp Leone-Weibull, the Marshall Olkin extended-Weibull, gamma-Weibull , Kumaraswamy-Weibull , Weibull-Fréchet, beta-Weibull, transmuted modified-Weibull, Kumaraswamy transmuted- Weibull, modified beta-Weibull, Mcdonald-Weibull and transmuted exponentiated generalized-Weibull models so it is a good alternative to these models in modeling aircraft windshield data as well as the new lifetime model is much better than the Weibull-Weibull, odd Weibull-Weibull, Weibull Log-Weibull, the gamma exponentiated-exponential and exponential exponential-geometric models so it is a good alternative to these models in modeling the survival times of Guinea pigs. We hope that the new distribution will attract wider applications in reliability, engineering and other areas of research.

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“…Some wellknown generators are Marshall-Olkin-G family by Marshall and Olkin (1997) and Gupta et al (1998) who proposed the exponentiated-G class. Other generators that can be cited are Barreto-Souza and Simas (2013), Alzaatreh et al (2013), Bourguignon et al (2014), Yousof et al (2015), Tahir et al (2016), Afify et al (2016b), Afify et al (2016a), Yousof et al (2016), Merovci et al (2016), Korkmaz and Genc (2016), Alizadeh et al (2016), Afify et al (2017), Hamedani et al (2017), Cordeiro et al (2017), Alizadeh et al (2017a,b) Nofal et al (2017), Yousof et al (2017a,b), Brito et al (2017), , Cordeiro et al (2018), Hamedani et al (2018), Korkmaz et al (2018a,b), Yousof et al (2018a,b), and , among others.…”

Section: Introductionmentioning

confidence: 99%

A New Weibull Class of Distributions: Theory, Characterizations and Applications

Yousof

Majumder²,

Jahanshahi

et al. 2018

JSRI

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Type I General Exponential Class of distributions

Cited by 55 publications

References 50 publications

A New Flexible Three-Parameter Model: Properties, Clayton Copula, and Modeling Real Data

A New Flexible Three-Parameter Model: Properties, Clayton Copula, and Modeling Real Data

The Burr X Exponentiated Weibull Model: Characterizations,Mathematical Properties and Applications to Failure and Survival Times Data

A New Weibull Class of Distributions: Theory, Characterizations and Applications

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