Estimación bayesiana en la familia Pareto generalizada
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| Autor: | |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2008 |
| Descripción: | The generalized Pareto family of distributions with scale parameter > 0 and k form, has been used for modeling surplus over a given threshold, even though the parametric estimation in this family has some problems. In this work we study the Bayesian approach for estimating parameters and k when no a priori information is available and we discuss the case when there is previous information. We presenta simulation study in order to analyze the performance of the Bayesian methodology, employing non informative a priori distributions and the methods available in the literature. This study shows that the Bayesian estimation performs better than other proposed methods, in terms of bias and aquare root of the mean quadratic error. The estimation methodologies analized are applied to real data sets. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/289 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/289 |
| Palabra clave: | Generalized Pareto family estimation methods Monte Carlo study Familia Pareto generalizada métodos de estimación estudio Monte Carlo |