Three-step N–R algorithm for the maximum-likelihood estimation of the general extreme values distribution parameters

2008 ◽  
Vol 39 (5) ◽  
pp. 384-394 ◽  
Author(s):  
Tefaruk Haktanir
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Extreme Values ◽  
Likelihood Estimation ◽  
Distribution Parameters ◽  
Extreme Values Distribution ◽  
Values Distribution

Utilizarea teoriei valorilor extreme în climatologie

2019 ◽  
Author(s):  
Valentin Raileanu ◽  
Keyword(s):  
Maximum Likelihood ◽  
Extreme Values ◽  
Probability Distributions ◽  
Simulated Data ◽  
Likelihood Estimation ◽  
R Software ◽  
Data Set ◽  
Data Format ◽  
Generalized Pareto ◽  
Distribution Parameters

The article briefly describes the history and fields of application of the theory of extreme values, including climatology. The data format, the Generalized Extreme Value (GEV) probability distributions with Bock Maxima, the Generalized Pareto (GP) distributions with Point of Threshold (POT) and the analysis methods are presented. Estimating the distribution parameters is done using the Maximum Likelihood Estimation (MLE) method. Free R software installation, the minimum set of required commands and the GUI in2extRemes graphical package are described. As an example, the results of the GEV analysis of a simulated data set in in2extRemes are presented.

scholarly journals "Evaluation of Some Methods for Estimating the Weibull Distribution Parameters"

2017 ◽  
Vol 4 (2) ◽  
pp. 8-14
Author(s):  
J. A. Labban ◽  
H. H. Depheal
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Weibull Distribution ◽  
Least Squares ◽  
Mean Square Error ◽  
Random Sample ◽  
Likelihood Estimation ◽  
Mean Square ◽  
Distribution Parameters

"This paper some of different methods to estimate the parameters of the 2-Paramaters Weibull distribution such as (Maximum likelihood Estimation, Moments, Least Squares, Term Omission). Mean square error will be considered to compare methods fits in case to select the best one. There by simulation will be implemented to generate different random sample of the 2-parameters Weibull distribution, those contain (n=10, 50, 100, 200) iteration each 1000 times."

Analiza temperaturilor maxime și minime anuale în Chișinău

2019 ◽  
Author(s):  
Valentin Raileanu ◽  
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Likelihood Estimation ◽  
Generalized Extreme Value Distribution ◽  
Return Periods ◽  
R Software ◽  
Data Format ◽  
Return Levels

The article briefly describes the fields of application of the theory of extreme values, including climatology. The data format and the analysis methods of the annual maxima and minima temperatures in Chisinau are presented. Free R software and the GUI in2extRemes graphical package are used. Estimating the parameters of the Generalized Extreme Value distribution and return levels vs return periods is done using the Maximum Likelihood Estimation (MLE) method.

scholarly journals The Burr X Fréchet Model for Extreme Values: Mathematical Properties, Classical Inference and Bayesian Analysis

2019 ◽  
pp. 797-818 ◽  
Author(s):  
Haitham Yousof ◽  
S. Jahanshahi ◽  
Vikas Kumar Sharma
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Extreme Values ◽  
Likelihood Estimation ◽  
Real Data ◽  
Mcmc Methods ◽  
Bayes Estimators ◽  
Mathematical Properties ◽  
Asymptotic Confidence Intervals ◽  
Classical Inference

In this paper, we investigate a new model based on Burr X and Fréchet distribution forextreme values and derive some of its properties. Maximum likelihood estimation alongwith asymptotic confidence intervals is considered for estimating the parameters of thedistribution. We demonstrate empirically the flexibility of the distribution in modelingvarious types of real data. Furthermore, we also provide Bayes estimators and highestposterior density intervals of the parameters of the distribution using Markov ChainMonte Carlo (MCMC) methods.

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