Likelihood-Based Sufficient Dimension Reduction
We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directio...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2009 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/84065 |
| Acceso en línea: | http://hdl.handle.net/11336/84065 |
| Access Level: | acceso abierto |
| Palabra clave: | Central Subspace Directional Regression Grassmann Manifolds Sliced Average Variance Estimation Sliced Inverse Regression https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | We obtain the maximum likelihood estimator of the central subspace under conditional normality of the predictors given the response. Analytically and in simulations we found that our new estimator can preform much better than sliced inverse regression, sliced average variance estimation and directional regression, and that it seems quite robust to deviations from normality. |
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