Optimal inverse Beta(3,3) transformation in kernel density estimation
A double transformation kernel density estimator that is suitable for heavy-tailed distributions is presented. Using a double transformation, an asymptotically optimal bandwidth parameter can be calculated when minimizing the expression of the asymptotic mean integrated squared error of the transfor...
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2010 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/11229 |
| Acceso en línea: | https://hdl.handle.net/2099/11229 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical statistics Kernel density estimation Transformations Beta density Right skewness. Estadística matemàtica Classificació AMS::62 Statistics::62G Nonparametric inference Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | A double transformation kernel density estimator that is suitable for heavy-tailed distributions is presented. Using a double transformation, an asymptotically optimal bandwidth parameter can be calculated when minimizing the expression of the asymptotic mean integrated squared error of the transformed variable. Simulation results are presented showing that this approach performs better than existing alternatives. An application to insurance claim cost data is included |
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