Robust nonparametric estimation with missing data
In this paper, under a nonparametric regression model, we introduce two families of robust procedures to estimate the regression function when missing data occur in the response. The first proposal is based on a local M-functional applied to the conditional distribution function estimate adapted to...
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| Formato: | Capítulo de libro |
| Lenguaje: | Inglés |
| Publicado: |
2009
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| Acceso en línea: | Registro en Scopus DOI Handle Registro en la Biblioteca Digital |
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| Sumario: | In this paper, under a nonparametric regression model, we introduce two families of robust procedures to estimate the regression function when missing data occur in the response. The first proposal is based on a local M-functional applied to the conditional distribution function estimate adapted to the presence of missing data. The second proposal imputes the missing responses using the local M-smoother based on the observed sample and then estimates the regression function with the completed sample. We show that the robust procedures considered are consistent and asymptotically normally distributed. A robust procedure to select the smoothing parameter is also discussed. © 2008 Elsevier B.V. All rights reserved. |
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| ISSN: | 03783758 |
| DOI: | 10.1016/j.jspi.2008.02.019 |