Correction to: Khan, M. S., King, R. and Hudson, I. L., (2017), Transmuted generalized exponential distribution: a generalization of the exponential distribution with applications to survival data, communications in Statistics – Simulation and computation, 46:6, 4377–4398

2021 ◽  
pp. 1-6
Author(s):  
Reza Azimi ◽  
Mahdy Esmailian
Keyword(s):  
Exponential Distribution ◽  
Survival Data ◽  
Data Communications ◽  
Generalized Exponential Distribution

scholarly journals Bayesian Analysis of Topp-Leone Generalized Exponential Distribution

2018 ◽  
Vol 47 (4) ◽  
pp. 1-15
Author(s):  
Najrullah Khan ◽  
Athar Ali Khan
Keyword(s):  
Bayesian Analysis ◽  
Exponential Distribution ◽  
Bayesian Approach ◽  
Survival Data ◽  
Survival Model ◽  
Generalized Exponential Distribution ◽  
Data Set ◽  
Simulation Tools ◽  
Support Set ◽  
Optimization And Simulation

The Topp-Leone distribution was introduced by Topp-Leone in 1955. In this paper, an attempt has been made to fit Topp-Leone Generalized Exponential distribution. Since, Topp-Leone distribution contains only one parameter and its support set is restricted to (0,1), because of this, in most practical situations it is not a better fit for the lifetime modelling. So an extension of this distribution is required. A Bayesian approach has been adopted to fit this model as survival model. A real survival data set is used to illustrate. Implementation is done using R and JAGS and appropriate illustrations are made. R and JAGS codes have been provided to implement censoring mechanism using both optimization and simulation tools.

Univariate and bivariate extensions of the generalized exponential distributions

2021 ◽  
Vol 71 (6) ◽  
pp. 1581-1598
Author(s):  
Vahid Nekoukhou ◽  
Ashkan Khalifeh ◽  
Hamid Bidram
Keyword(s):  
Em Algorithm ◽  
Exponential Distribution ◽  
Bivariate Distribution ◽  
Generalized Exponential Distribution ◽  
Exponential Distributions ◽  
Unknown Parameters ◽  
Data Set ◽  
New Class ◽  
Special Cases ◽  
Univariate 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.

Absolute Continuous Multivariate Generalized Exponential Distribution

Sankhya B ◽  
2015 ◽  
Vol 77 (2) ◽  
pp. 175-206 ◽  
Author(s):  
Debasis Kundu ◽  
Ankush Kumar ◽  
Arjun K. Gupta
Keyword(s):  
Exponential Distribution ◽  
Generalized Exponential Distribution

Exponentiated Generalized Exponential Distribution: Ordinary Differential Equations

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

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

Complexity ◽  
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.

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

2008 ◽  
Vol 39 (1) ◽  
pp. 45-51 ◽  
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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