scholarly journals Minimax Estimation of Parameter of Generalized Exponential Distribution under Square Log Error Loss and MLINEX Loss Functions

2013 ◽  
Vol 5 (2) ◽  
pp. 24-27
Author(s):  
Lanping Li
Keyword(s):  
Exponential Distribution ◽  
Minimax Estimation ◽  
Loss Functions ◽  
Generalized Exponential Distribution ◽  
Error Loss

scholarly journals The Comparison Between Standard Bayes Estimators of the Reliability Function of Exponential Distribution

2018 ◽  
Vol 31 (3) ◽  
pp. 135 ◽  
Author(s):  
Mohammed Jamel Ali ◽  
Hazim Mansoor Gorgees ◽  
Adel Abdul Kadhim Hussein
Keyword(s):  
Exponential Distribution ◽  
Loss Function ◽  
Reliability Function ◽  
Loss Functions ◽  
Bayes Estimators ◽  
Squared Error Loss Function ◽  
Monte Carlo Simulation Technique ◽  
Error Loss ◽  
Squared Error ◽  
The One

   In this paper, a Monte Carlo Simulation technique is used to compare the performance of the standard Bayes estimators of the reliability function of the one parameter exponential distribution .Three types of loss functions are adopted, namely, squared error  loss function (SELF) ,Precautionary error loss function (PELF) andlinear exponential error  loss function(LINEX) with informative and non- informative prior .The criterion integrated mean square error (IMSE) is employed to assess the performance of such estimators

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
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