Statistical inference about the shape parameter of the exponential power distribution

1999 ◽  
Vol 40 (4) ◽  
pp. 459-468 ◽  
Author(s):  
Zhenmin Chen
Keyword(s):  
Statistical Inference ◽  
Power Distribution ◽  
Shape Parameter ◽  
Exponential Power Distribution ◽  
Exponential Power

A Note On Order Statistics from Exponential Power Distribution

2017 ◽  
Vol 6 (1-2) ◽  
pp. 138
Author(s):  
Soyinka Ajibola Taiwo ◽  
Olosunde A Akin
Keyword(s):  
Probability Density Function ◽  
Order Statistics ◽  
Power Distribution ◽  
Shape Parameter ◽  
Laplace Distribution ◽  
Tail Region ◽  
Exponential Power Distribution ◽  
Exponential Power ◽  
First Moment ◽  
Probability Density Function Pdf

 In this paper, we derived probability density function (pdf) for the order statistics from eponential power distribution (EPD). The distribution is flexible at the tail region, because of the presence of shape parameter, which regulates the thickness of the tail. The first moment of the obtained distribution of the order statistics from EPD is presented as well as other measures of central tendencies. This results generalized the results on order statistics from the Laplace distribution and also the results obtained by Arnold, Balakrishnan and Nagaraja on order statistics from normal distribution.

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.

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