scholarly journals Unified Bayesian conditional autoregressive risk measures using the skew exponential power distribution

2020 ◽  
Author(s):  
Marco Bottone ◽  
Lea Petrella ◽  
Mauro Bernardi
Keyword(s):  
Power Distribution ◽  
Risk Measures ◽  
Exponential Power Distribution ◽  
Conditional Autoregressive ◽  
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.

scholarly journals Non-Gaussian Linear Mixing Models for Hyperspectral Images

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Peter Bajorski
Keyword(s):  
Power Distribution ◽  
Error Term ◽  
Hyperspectral Data ◽  
Mixing Models ◽  
Exponential Power Distribution ◽  
Gaussian Distributions ◽  
Independent Components ◽  
Random Behavior ◽  
Exponential Power ◽  
Non Gaussian

Modeling of hyperspectral data with non-Gaussian distributions is gaining popularity in recent years. Such modeling mostly concentrates on attempts to describe a distribution, or its tails, of all image spectra. In this paper, we recognize that the presence of major materials in the image scene is likely to exhibit nonrandomness and only the remaining variability due to noise, or other factors, would exhibit random behavior. Hence, we assume a linear mixing model with a structured background, and we investigate various distributional models for the error term in that model. We propose one model based on the multivariatet-distribution and another one based on independent components following an exponential power distribution. The former model does not perform well in the context of the two images investigated in this paper, one AVIRIS and one HyMap image. On the other hand, the latter model works reasonably well with the AVIRIS image and very well with the HyMap image. This paper provides the tools that researchers can use for verifying a given model to be used with a given image.

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