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Univariate and bivariate extensions of the generalized exponential distributions

  • Vahid Nekoukhou EMAIL logo , Ashkan Khalifeh and Hamid Bidram
Published/Copyright: December 10, 2021
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Abstract

The main aim of this paper is to introduce a new class of continuous generalized exponential distributions, both for the univariate and bivariate cases. This new class of distributions contains some newly developed distributions as special cases, such as the univariate and also bivariate geometric generalized exponential distribution and the exponential-discrete generalized exponential distribution. Several properties of the proposed univariate and bivariate distributions, and their physical interpretations, are investigated. The univariate distribution has four parameters, whereas the bivariate distribution has five parameters. We propose to use an EM algorithm to estimate the unknown parameters. According to extensive simulation studies, we see that the effectiveness of the proposed algorithm, and the performance is quite satisfactory. A bivariate data set is analyzed and it is observed that the proposed models and the EM algorithm work quite well in practice.

Acknowledgement

The authors sincerely thank the anonymous referees for their valuable comments that led to improvement of the paper and also special thanks to the Editor for all his/her considerations in this paper.

  1. Communicated by Gejza Wimmer

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Received: 2020-09-03
Accepted: 2020-11-19
Published Online: 2021-12-10
Published in Print: 2021-12-20

© 2021 Mathematical Institute Slovak Academy of Sciences

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