Strong convergence of robust equivariant nonparametric functional regression estimators
Robust nonparametric equivariant M-estimators for the regression function have been extensively studied when the covariates are in R k . In this paper, we derive strong uniform convergence rates for kernel-based robust equivariant M-regression estimator when the covariates are functional.
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/18943 |
| Acceso en línea: | http://hdl.handle.net/11336/18943 |
| Access Level: | acceso abierto |
| Palabra clave: | Functional Data Kernel Weights M-Location Functionals Robust Estimation https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | Robust nonparametric equivariant M-estimators for the regression function have been extensively studied when the covariates are in R k . In this paper, we derive strong uniform convergence rates for kernel-based robust equivariant M-regression estimator when the covariates are functional. |
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