October 2021 Adaptative bounds and estimation in the case of the three-parameter Weibull distribution
Ouindllassida Jean-Etienne Ouédraogo, Edoh Katchekpele, Tchilabalo Abozou Kpanzou
Afr. Stat. 16(4): 2979-2991 (October 2021). DOI: 10.16929/as/2021.2979.191

Abstract

In this study, bounds of the parameters of the Weibull distribution with three parameters are proposed. These bounds are data dependent and are therefore adaptive. Exact probabilities of recovery of the parameters concerned were proposed and then applied to simulated data. Once the bounds are obtained, a Differential Evolution Optimization Method (DEOM) is then used to estimate the parameters of the above-mentioned distribution. The performances of the estimators obtained were evaluated through Monte-Carlo simulations.

Dans cet article, nous proposons des bornes pour les paramètres de la distribution de Weibull à trois paramètres. Ces bornes dépendent des données et sont donc adaptatives. Des probabilités exactes de localisation des paramètres concernés ont été proposées puis appliquées à des données simulées. Une fois les bornes obtenues, une méthode d’optimisation globale connue sous le nom de Differential Evolution est ensuite utilisée pour estimer les paramètres de la distribution. Les performances des estimateurs obtenus ont été evaluées par la méthode de Monte-Carlo.

Citation

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Ouindllassida Jean-Etienne Ouédraogo. Edoh Katchekpele. Tchilabalo Abozou Kpanzou. "Adaptative bounds and estimation in the case of the three-parameter Weibull distribution." Afr. Stat. 16 (4) 2979 - 2991, October 2021. https://doi.org/10.16929/as/2021.2979.191

Information

Published: October 2021
First available in Project Euclid: 15 February 2022

Digital Object Identifier: 10.16929/as/2021.2979.191

Subjects:
Primary: 62F10
Secondary: 65C2

Keywords: differential evolution optimization method , estimation , maximum likelihood , order statistics , search space , three-parameter Weibull model

Rights: Copyright © 2021 The Statistics and Probability African Society

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Vol.16 • No. 4 • October 2021
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