Parameter estimation for 3-parameter generalized pareto distribution by the principle of maximum entropy (POME)
نویسندگان
چکیده
Abstract The principle of maximum entropy (POME) is employed to derive a new method of parameter estimation for the 3-parameter generalized Pareto (GP) distribution. Monte Carlo simulated data are used to evaluate this method and compare it with the methods of moments (MOM), probability weighted moments (PWM), and maximum likelihood estimation (MLE). The parameter estimates yielded by the POME are either superior or comparable for high skewness. Estimation des paramètres d'une loi de Pareto généralisée à trois paramètres par la méthode du maximum d'entropie Résumé Nous avons utilisé le principe du maximum d'entropie en vue d'établir une nouvelle méthode d'estimation des paramètres de la distribution de Pareto généralisée à trois paramètres. Des données synthétiques générées selon une procédure de Monte Carlo ont été utilisées pour évaluer cette méthode et pour la comparer aux méthodes des moments, des moments pondérés et du maximum de vraisemblance. L'estimation des paramètres s'appuyant sur le principe du maximum d'entropie est préférable ou comparable à celle des autres méthodes en particulier lorsque l'asymétrie est forte.
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