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

Mahmoud M. Mansour | Zendy; Nadeem Shafique Butt | Zendy; Haitham M. Yousof | Zendy; Saiful Islam Ansari | Zendy; Mohamed Ibrahim | Zendy

Open Access

A 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 Islam 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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research