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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Autor principal: Boente, G.
Otros Autores: González-Manteiga, W., Pérez-González, A.
Formato: Capítulo de libro
Lenguaje:Inglés
Publicado: 2009
Acceso en línea:Registro en Scopus
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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