A note on Smoothed Functional Inverse Regression
Estimation in the context of functional data analysis is almost always non-parametric, since the object to be estimated lives in an infinite dimensional space. That is the case for the functional linear model with a real response and a process as covariables. In a recent paper Ferré and Yao state th...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2007 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/84268 |
| Acceso en línea: | http://hdl.handle.net/11336/84268 |
| Access Level: | acceso abierto |
| Palabra clave: | Dimension Reduction Functional Data Analysis Inverse Regression https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | Estimation in the context of functional data analysis is almost always non-parametric, since the object to be estimated lives in an infinite dimensional space. That is the case for the functional linear model with a real response and a process as covariables. In a recent paper Ferré and Yao state that the estimation of the Effective Dimension Reduction (EDR) subspace via SIR has parametric order. We show that a strong condition is needed for their statement to be true. |
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