Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihood

We derive here estimators for the parameters of the Gumbel distribution using three estimating methods, namely, the probability weighted moments, the moment and the maximum likelihood methods. Furthermore, we compare the performance of these estimators using simulations. Both integer and non-integer...

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Main Authors: Smail Mahdi, Myrtene Cenac
Format: Article
Language:English
Published: Universidad de Costa Rica 2012-03-01
Series:Revista de Matemática: Teoría y Aplicaciones
Online Access:https://revistas.ucr.ac.cr/index.php/matematica/article/view/259
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author Smail Mahdi
Myrtene Cenac
author_facet Smail Mahdi
Myrtene Cenac
author_sort Smail Mahdi
collection DOAJ
description We derive here estimators for the parameters of the Gumbel distribution using three estimating methods, namely, the probability weighted moments, the moment and the maximum likelihood methods. Furthermore, we compare the performance of these estimators using simulations. Both integer and non-integer orders are considered in the probability weighted moments method. Overall, the results show that the probability weighted moments method outperforms the other methods in the estimation of both $\alpha$ and $\epsilon$ parameters.
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spelling doaj.art-e64c627ed4fe481495a705993018c3352023-09-03T00:39:41ZengUniversidad de Costa RicaRevista de Matemática: Teoría y Aplicaciones2215-33732012-03-01121-215115610.15517/rmta.v12i1-2.259244Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihoodSmail Mahdi0Myrtene Cenac1University of the West Indies, Department of Computer Science, Mathematics & PhysicsDepartment of Computer Science, Mathematics & Physics, University of the West Indies, Cave Hill Campus, BarbadosWe derive here estimators for the parameters of the Gumbel distribution using three estimating methods, namely, the probability weighted moments, the moment and the maximum likelihood methods. Furthermore, we compare the performance of these estimators using simulations. Both integer and non-integer orders are considered in the probability weighted moments method. Overall, the results show that the probability weighted moments method outperforms the other methods in the estimation of both $\alpha$ and $\epsilon$ parameters.https://revistas.ucr.ac.cr/index.php/matematica/article/view/259
spellingShingle Smail Mahdi
Myrtene Cenac
Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihood
Revista de Matemática: Teoría y Aplicaciones
title Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihood
title_full Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihood
title_fullStr Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihood
title_full_unstemmed Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihood
title_short Estimating Parameters of Gumbel Distribution using the Methods of Moments, probability weighted Moments and maximum likelihood
title_sort estimating parameters of gumbel distribution using the methods of moments probability weighted moments and maximum likelihood
url https://revistas.ucr.ac.cr/index.php/matematica/article/view/259
work_keys_str_mv AT smailmahdi estimatingparametersofgumbeldistributionusingthemethodsofmomentsprobabilityweightedmomentsandmaximumlikelihood
AT myrtenecenac estimatingparametersofgumbeldistributionusingthemethodsofmomentsprobabilityweightedmomentsandmaximumlikelihood