Open AccessA Generalization of Reciprocal Exponential Model: Clayton Copula, Statistical Properties and Modeling Skewed and Symmetric Real Data Sets
Author(s) -
Mahmoud M. Mansour,
Nadeem Shafique Butt,
Haitham M. Yousof,
Saiful İslam Ansari,
Mohamed Ibrahim
Publication year - 2020
Publication title -
pakistan journal of statistics and operation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 15
eISSN - 2220-5810
pISSN - 1816-2711
DOI - 10.18187/pjsor.v16i2.3298
Subject(s) - mathematics , copula (linguistics) , reciprocal , exponential family , bivariate analysis , estimator , exponential function , statistics , univariate , econometrics , generalization , exponential distribution , multivariate statistics , mathematical analysis , philosophy , linguistics
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.