scholarly journals Improved maximum-likelihood estimation of the shape parameter in the Nakagami distribution

2013 ◽  
Vol 83 (3) ◽  
pp. 434-445 ◽  
Author(s):  
Jacob Schwartz ◽  
Ryan T. Godwin ◽  
David E. Giles
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Shape Parameter ◽  
Likelihood Estimation ◽  
Nakagami Distribution

scholarly journals Bayesian and Maximum Likelihood Estimation of the Shape Parameter of Exponential Inverse Exponential Distribution: A Comparative Approach

2020 ◽  
pp. 28-43
Author(s):  
Innocent Boyle Eraikhuemen ◽  
Fadimatu Bawuro Mohammed ◽  
Ahmed Askira Sule
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Bayesian Estimation ◽  
Exponential Distribution ◽  
Shape Parameter ◽  
Likelihood Estimation ◽  
Monte Carlo Study ◽  
Comparative Approach ◽  
The Mean ◽  
Inverse Exponential Distribution

This paper aims at making Bayesian analysis on the shape parameter of the exponential inverse exponential distribution using informative and non-informative priors. Bayesian estimation was carried out through a Monte Carlo study under 10,000 replications. To assess the effects of the assumed prior distributions and loss function on the Bayesian estimators, the mean square error has been used as a criterion. Overall, simulation results indicate that Bayesian estimation under QLF outperforms the maximum likelihood estimation and Bayesian estimation under alternative loss functions irrespective of the nature of the prior and the sample size. Also, for large sample sizes, all methods perform equally well.

scholarly journals A New Numerical Algorithm of Three Parameter Generalized Extreme Value Distribution of Wind Speed

2021 ◽  
Vol 248 ◽  
pp. 01023
Author(s):  
Ye Tao
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Numerical Algorithm ◽  
Shape Parameter ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Extreme Value ◽  
Value Distribution ◽  
Generalized Extreme Value Distribution ◽  
Generalized Extreme Value

Maximum likelihood estimation method is used to solve the problem of parameter estimation of three-parameter generalized extreme value distribution. Based on the theory of order reducing，a new numerical algorithm is presented to resolve the problem of maximum likelihood estimation of three-parameter generalized extreme value distribution．Firstly，the shape parameter is assumed to be known and ternary likelihood equations can be transferred into binary ones that are solved with the dichotomy．And then，scale and location parameters are the functions of shape parameter. Further，the maximum likelihood function is described as a unitary function of shape parameter. The optimal estimation of shape parameters can be obtained by applying dichotomy again.

Sign in / Sign up

Export Citation Format

Share Document