A Generalization of Reciprocal Exponential Model: Clayton Copula, Statistical Properties and Modeling Skewed and Symmetric Real Data Sets

By Mahmoud M. M. Mansour, Nadeem Shafique Butt, Haitham M. Yousof, S. Ansari, Mohamed Ibrahim. 2020

63 citations recorded by the source corpus.

We introduce a new extension of the reciprocal Exponential distribution for modeling the extreme values. We used the Morgenstern family and the clayton copula for deriving many bivariate and multivariate extensions of the new model. Some of its properties are derived. We assessed the performance of the maximum likelihood estimators (MLEs) via a graphical simulation study. The assessment was based on the sample size. The new reciprocal model is employed for modeling the skewed and the symmetric real data sets. The new reciprocal model is better than some other important competitive models in statistical modeling.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/bbe63c081704b1de6f23ba05360d74a44ba9280d

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Citations in the corpus

A Generalization of Lomax Distribution with Properties, Copula and Real Data Applications (2020, background): “Recently, many useful extensions are presented by Bhatti et al. (2018), Goual Yousof (2019), Ibrahim and Yousof (2020), Karamikabir et al. (2020), Mansour et al. (2020f), Ansari et al. (2020), Goual et al. (2020) and Yadav et al. (2020).”