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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030 |a JSPID 
100 1 |a Boente, G. 
245 1 0 |a Robust nonparametric estimation with missing data 
260 |c 2009 
270 1 0 |m Boente, G.; Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Ciudad Universitaria, Pabellon 2, Buenos Aires, C1428EHA, Argentina; email: gboente@dm.uba.ar 
506 |2 openaire  |e Política editorial 
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504 |a Boente, G., González-Manteiga, W., Pérez-González, A., 2005. Robust nonparametric estimation with missing data. Available at 〈http://www.ic.fcen.uba.ar/preprints/boegonper.pdf〉; Cantoni, E., Ronchetti, E., Resistant selection of the smoothing parameter for smoothing splines (2001) Statist. Computing, 11, pp. 141-146 
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520 3 |a 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.  |l eng 
536 |a Detalles de la financiación: Universidad de Buenos Aires, PID 5505 
536 |a Detalles de la financiación: MTM2005-00820 
536 |a Detalles de la financiación: Agencia Nacional de Promoción Científica y Tecnológica 
536 |a Detalles de la financiación: Consejo Nacional de Investigaciones Científicas y Técnicas, PAV 120, PICT 21407 
536 |a Detalles de la financiación: Federación Española de Enfermedades Raras, FEDER 
536 |a Detalles de la financiación: The authors would like to thank the Associate Editor and two anonymous referees for their valuable comments and suggestions that lead to improve the presentation of the paper. This work began while Graciela Boente was visiting the Departamento de Estadí stica e Investigación Operativa de la Universidad de Santiago de Compostela. This research was partially supported by Grants X094 from the Universidad de Buenos Aires, PID 5505 from CONICET, PAV 120 and PICT 21407 from ANPCYT at Buenos Aires, Argentina and also by the DGICYT Spanish Grant MTM2005-00820 (European FEDER support included). Appendix A 
593 |a Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, CONICET, Ciudad Universitaria, Pabellon 2, Buenos Aires, C1428EHA, Argentina 
593 |a Universidad de Santiago de Compostela, Spain 
593 |a Universidad de Vigo, Spain 
690 1 0 |a ASYMPTOTIC PROPERTIES 
690 1 0 |a KERNEL WEIGHTS 
690 1 0 |a MISSING DATA 
690 1 0 |a NONPARAMETRIC REGRESSION 
690 1 0 |a ROBUST ESTIMATION 
700 1 |a González-Manteiga, W. 
700 1 |a Pérez-González, A. 
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