Bandwidth choice for robust nonparametric scale function estimation
We introduce and compare several robust procedures for bandwidth selection when estimating the variance function. These bandwidth selectors are to be used in combination with the robust scale estimates introduced by Boente et al. (2010a). We consider two different robust cross--validation strategies...
| Autores: | , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/14886 |
| Acceso en línea: | http://hdl.handle.net/11336/14886 |
| Access Level: | acceso abierto |
| Palabra clave: | Cross-Validation Data-Driven Bandwidth Heteroscedasticity Local M-Estimators Nonparametric Regression Robust Estimation https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | We introduce and compare several robust procedures for bandwidth selection when estimating the variance function. These bandwidth selectors are to be used in combination with the robust scale estimates introduced by Boente et al. (2010a). We consider two different robust cross--validation strategies combined with two ways for measuring the cross--validation prediction error. The different proposals are compared with non robust alternatives using Monte Carlo simulation. We also derive some asymptotic results to investigate the large sample performance of the corresponding robust data--driven scale estimators. |
|---|