A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution

Estimation based on probability-weighted moments is a well-established method and an excellent alternative to the classic method of moments or the maximum likelihood method, especially for small sample sizes. In this research, we developed a new class of estimators for the parameters of the Pareto t...

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Main Authors: Frederico Caeiro, Ayana Mateus
Format: Article
Language:English
Published: MDPI AG 2023-02-01
Series:Mathematics
Subjects:
Online Access:https://www.mdpi.com/2227-7390/11/5/1076
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author Frederico Caeiro
Ayana Mateus
author_facet Frederico Caeiro
Ayana Mateus
author_sort Frederico Caeiro
collection DOAJ
description Estimation based on probability-weighted moments is a well-established method and an excellent alternative to the classic method of moments or the maximum likelihood method, especially for small sample sizes. In this research, we developed a new class of estimators for the parameters of the Pareto type I distribution. A generalization of the probability-weighted moments approach is the foundation for this new class of estimators. It has the advantage of being valid in the entire parameter space of the Pareto distribution. We established the asymptotic normality of the new estimators and applied them to simulated and real datasets in order to illustrate their finite sample behavior. The results of comparisons with the most used estimation methods were also analyzed.
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spelling doaj.art-428c7229cc2a47098c4f05f02ec607912023-11-17T08:07:51ZengMDPI AGMathematics2227-73902023-02-01115107610.3390/math11051076A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto DistributionFrederico Caeiro0Ayana Mateus1NOVA School of Science and Technology (FCT NOVA) and CMA, Campus de Caparica, NOVA University Lisbon, 2829-516 Caparica, PortugalNOVA School of Science and Technology (FCT NOVA) and CMA, Campus de Caparica, NOVA University Lisbon, 2829-516 Caparica, PortugalEstimation based on probability-weighted moments is a well-established method and an excellent alternative to the classic method of moments or the maximum likelihood method, especially for small sample sizes. In this research, we developed a new class of estimators for the parameters of the Pareto type I distribution. A generalization of the probability-weighted moments approach is the foundation for this new class of estimators. It has the advantage of being valid in the entire parameter space of the Pareto distribution. We established the asymptotic normality of the new estimators and applied them to simulated and real datasets in order to illustrate their finite sample behavior. The results of comparisons with the most used estimation methods were also analyzed.https://www.mdpi.com/2227-7390/11/5/1076asymptotic distributionPareto distributionparameter estimationprobability-weighted moment
spellingShingle Frederico Caeiro
Ayana Mateus
A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution
Mathematics
asymptotic distribution
Pareto distribution
parameter estimation
probability-weighted moment
title A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution
title_full A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution
title_fullStr A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution
title_full_unstemmed A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution
title_short A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution
title_sort new class of generalized probability weighted moment estimators for the pareto distribution
topic asymptotic distribution
Pareto distribution
parameter estimation
probability-weighted moment
url https://www.mdpi.com/2227-7390/11/5/1076
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