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

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...

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Published in:Pakistan journal of statistics and operation research 2020-06, Vol.16 (2), p.373-386
Main Authors: Mansour, M M, Butt, Nadeem Shafique, Yousof, Haitham M, Ansari, S I, Ibrahim, Mohamed
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Language:English
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Bivariate analysis
Datasets
Extreme values
Maximum likelihood estimators
Probability distribution functions
Random variables
Statistical models
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Butt, Nadeem Shafique
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Ansari, S I
Ibrahim, Mohamed
description 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.
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subjects Bivariate analysis
Datasets
Extreme values
Maximum likelihood estimators
Probability distribution functions
Random variables
Statistical models
title A Generalization of Reciprocal Exponential Model: Clayton Copula, Statistical Properties and Modeling Skewed and Symmetric Real Data Sets
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