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...

Full description

Saved in:
Bibliographic Details
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
Format: Article
Language:English
Subjects:
Bivariate analysis
Datasets
Extreme values
Maximum likelihood estimators
Probability distribution functions
Random variables
Statistical models
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:1816-2711
2220-5810
DOI:10.18187/pjsor.v16i2.3298