Absolute Continuous Multivariate Generalized Exponential Distribution

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.

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Exponentiated Generalized Exponential Distribution: Ordinary Differential Equations

Transactions on Engineering Technologies ◽

10.1007/978-981-13-2191-7_24 ◽

2018 ◽

pp. 341-352

Author(s):

Hilary I. Okagbue ◽

Pelumi E. Oguntunde ◽

Paulinus O. Ugwoke ◽

Abiodun A. Opanuga ◽

Ezinne C. Erondu

Keyword(s):

Differential Equations ◽

Ordinary Differential Equations ◽

Exponential Distribution ◽

Generalized Exponential Distribution

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The Weibull Generalized Exponential Distribution with Censored Sample: Estimation and Application on Real Data

Complexity ◽

10.1155/2021/6653534 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Hisham M. Almongy ◽

Ehab M. Almetwally ◽

Randa Alharbi ◽

Dalia Alnagar ◽

E. H. Hafez ◽

...

Keyword(s):

Monte Carlo ◽

Exponential Distribution ◽

Estimation Method ◽

Interval Estimation ◽

Likelihood Estimation ◽

Real Data ◽

Mcmc Methods ◽

Generalized Exponential Distribution ◽

Censored Sample ◽

Sample Maximum

This paper is concerned with the estimation of the Weibull generalized exponential distribution (WGED) parameters based on the adaptive Type-II progressive (ATIIP) censored sample. Maximum likelihood estimation (MLE), maximum product spacing (MPS), and Bayesian estimation based on Markov chain Monte Carlo (MCMC) methods have been determined to find the best estimation method. The Monte Carlo simulation is used to compare the three methods of estimation based on the ATIIP-censored sample, and also, we made a bootstrap confidence interval estimation. We will analyze data related to the distribution about single carbon fiber and electrical data as real data cases to show how the schemes work in practice.

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The geometric structures of the Weibull distribution manifold and the generalized exponential distribution manifold

Tamkang Journal of Mathematics ◽

10.5556/j.tkjm.39.2008.44 ◽

2008 ◽

Vol 39 (1) ◽

pp. 45-51 ◽

Cited By ~ 8

Author(s):

Limei Cao ◽

Huafei Sun ◽

Xiaojie Wang

Keyword(s):

Weibull Distribution ◽

Exponential Distribution ◽

Exponential Family ◽

Information Geometry ◽

Generalized Exponential Distribution ◽

Geometric Structures ◽

Basic Task

Investigating the geometric structures of the distribution manifolds is a basic task in information geometry. However, by so far, most works are on the distribution manifolds of exponential family. In this paper, we investigate two non-exponential distribution manifolds —the Weibull distribution manifold and the generalized exponential distribution manifold. Then we obtain their geometric structures.

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