A small sample comparison of maximum likelihood, moments and L-moments methods for the asymmetric exponential power distribution

2008 ◽  
Vol 52 (3) ◽  
pp. 1661-1673 ◽  
Author(s):  
P. Delicado ◽  
M.N. Goria
Keyword(s):  
Maximum Likelihood ◽  
Power Distribution ◽  
Small Sample ◽  
Exponential Power Distribution ◽  
L Moments ◽  
Asymmetric Exponential Power Distribution ◽  
Exponential Power

scholarly journals The Location-Scale Mixture Exponential Power Distribution: A Bayesian and Maximum Likelihood Approach

2012 ◽  
Vol 2012 ◽  
pp. 1-14
Author(s):  
Z. Rahnamaei ◽  
N. Nematollahi ◽  
R. Farnoosh
Keyword(s):  
Maximum Likelihood ◽  
Power Distribution ◽  
Real Data ◽  
Scale Mixture ◽  
Exponential Power Distribution ◽  
Likelihood Approach ◽  
Slash Distribution ◽  
Exponential Power ◽  
Mean Square Errors ◽  
New Distribution

We introduce an alternative skew-slash distribution by using the scale mixture of the exponential power distribution. We derive the properties of this distribution and estimate its parameter by Maximum Likelihood and Bayesian methods. By a simulation study we compute the mentioned estimators and their mean square errors, and we provide an example on real data to demonstrate the modeling strength of the new distribution.

scholarly journals Generalized Exponential Power Distribution with Application to Complete and Censored Data

2021 ◽  
pp. 34-55
Author(s):  
M. M. E. Abd El-Monsef ◽  
M. M. El-Awady
Keyword(s):  
Maximum Likelihood ◽  
Censored Data ◽  
Power Distribution ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
Type I ◽  
Sample Type ◽  
Exponential Power Distribution ◽  
Exponential Power

The exponential power distribution (EP) is a lifetime model that can exhibit increasing and bathtub hazard rate function. This paper proposed a generalization of EP distribution, named generalized exponential power (GEP) distribution. Some properties of GEP distribution will be investigated. Recurrence relations for single moments of generalized ordered statistics from GEP distribution are established and used for characterizing the GEP distribution. Estimation of the model parameters are derived using maximum likelihood method based on complete sample, type I, type II and random censored samples. A simulation study is performed in order to examine the accuracy of the maximum likelihood estimators of the model parameters. Three applications to real data, two with censored data, are provided in order to show the superiority of the proposed model to other models.

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