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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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03783758_v139_n2_p571_Boente http://hdl.handle.net/20.500.12110/paper_03783758_v139_n2_p571_Boente |
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paper:paper_03783758_v139_n2_p571_Boente2023-06-08T15:39:40Z Robust nonparametric estimation with missing data Boente, Graciela Lina Asymptotic properties Kernel weights Missing data Nonparametric regression Robust estimation 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. Fil:Boente, G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2009 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03783758_v139_n2_p571_Boente http://hdl.handle.net/20.500.12110/paper_03783758_v139_n2_p571_Boente |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Asymptotic properties Kernel weights Missing data Nonparametric regression Robust estimation |
| spellingShingle |
Asymptotic properties Kernel weights Missing data Nonparametric regression Robust estimation Boente, Graciela Lina Robust nonparametric estimation with missing data |
| topic_facet |
Asymptotic properties Kernel weights Missing data Nonparametric regression Robust estimation |
| description |
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. |
| author |
Boente, Graciela Lina |
| author_facet |
Boente, Graciela Lina |
| author_sort |
Boente, Graciela Lina |
| title |
Robust nonparametric estimation with missing data |
| title_short |
Robust nonparametric estimation with missing data |
| title_full |
Robust nonparametric estimation with missing data |
| title_fullStr |
Robust nonparametric estimation with missing data |
| title_full_unstemmed |
Robust nonparametric estimation with missing data |
| title_sort |
robust nonparametric estimation with missing data |
| publishDate |
2009 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03783758_v139_n2_p571_Boente http://hdl.handle.net/20.500.12110/paper_03783758_v139_n2_p571_Boente |
| work_keys_str_mv |
AT boentegracielalina robustnonparametricestimationwithmissingdata |
| _version_ |
1768543231785566208 |