scholarly journals Estimating parameter of Rayleigh distribution by using Maximum Likelihood method and Bayes method

2018 ◽  
Vol 300 ◽  
pp. 012036
Author(s):  
Fitri Ardianti ◽  
Sutarman
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Rayleigh Distribution ◽  
Likelihood Method ◽  
Bayes Method

scholarly journals Slashed Exponentiated Rayleigh Distribution

2015 ◽  
Vol 38 (2) ◽  
pp. 453-466 ◽  
Author(s):  
Hugo S. Salinas ◽  
Yuri A. Iriarte ◽  
Heleno Bolfarine
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Method ◽  
Real Data ◽  
Random Variable ◽  
Rayleigh Distribution ◽  
Likelihood Method ◽  
Independent Random Variables ◽  
Parameter Estimates ◽  
Positive Data ◽  
New Distribution

<p>In this paper we introduce a new distribution for modeling positive data with high kurtosis. This distribution can be seen as an extension of the exponentiated Rayleigh distribution. This extension builds on the quotient of two independent random variables, one exponentiated Rayleigh in the numerator and Beta(q,1) in the denominator with q&gt;0. It is called the slashed exponentiated Rayleigh random variable. There is evidence that the distribution of this new variable can be more flexible in terms of modeling the kurtosis regarding the exponentiated Rayleigh distribution. The properties of this distribution are studied and the parameter estimates are calculated using the maximum likelihood method. An application with real data reveals good performance of this new distribution.</p>

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