Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis
In this paper, under a semiparametric partly linear regression model with fixed design, we introduce a family of robust procedures to select the bandwidth parameter. The robust plug-in proposal is based on nonparametric robust estimates of the νth derivatives and under mild conditions, it converges...
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2008
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v52_n5_p2808_Boente http://hdl.handle.net/20.500.12110/paper_01679473_v52_n5_p2808_Boente |
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paper:paper_01679473_v52_n5_p2808_Boente2023-06-08T15:17:08Z Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis Asymptotic properties Bandwidth selectors Kernel weights Partly linear models Robust estimation Smoothing techniques Asymptotic analysis Bandwidth Linear regression Monte Carlo methods Parameter estimation Influential analysis Nonparametric robust estimates Optimal bandwidth Robust control In this paper, under a semiparametric partly linear regression model with fixed design, we introduce a family of robust procedures to select the bandwidth parameter. The robust plug-in proposal is based on nonparametric robust estimates of the νth derivatives and under mild conditions, it converges to the optimal bandwidth. A robust cross-validation bandwidth is also considered and the performance of the different proposals is compared through a Monte Carlo study. We define an empirical influence measure for data-driven bandwidth selectors and, through it, we study the sensitivity of the data-driven bandwidth selectors. It appears that the robust selector compares favorably to its classical competitor, despite the need to select a pilot bandwidth when considering plug-in bandwidths. Moreover, the plug-in procedure seems to be less sensitive than the cross-validation in particular, when introducing several outliers. When combined with the three-step procedure proposed by Bianco and Boente [2004. Robust estimators in semiparametric partly linear regression models. J. Statist. Plann. Inference 122, 229-252] the robust selectors lead to robust data-driven estimates of both the regression function and the regression parameter. © 2007 Elsevier B.V. All rights reserved. 2008 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v52_n5_p2808_Boente http://hdl.handle.net/20.500.12110/paper_01679473_v52_n5_p2808_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 Bandwidth selectors Kernel weights Partly linear models Robust estimation Smoothing techniques Asymptotic analysis Bandwidth Linear regression Monte Carlo methods Parameter estimation Influential analysis Nonparametric robust estimates Optimal bandwidth Robust control |
| spellingShingle |
Asymptotic properties Bandwidth selectors Kernel weights Partly linear models Robust estimation Smoothing techniques Asymptotic analysis Bandwidth Linear regression Monte Carlo methods Parameter estimation Influential analysis Nonparametric robust estimates Optimal bandwidth Robust control Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis |
| topic_facet |
Asymptotic properties Bandwidth selectors Kernel weights Partly linear models Robust estimation Smoothing techniques Asymptotic analysis Bandwidth Linear regression Monte Carlo methods Parameter estimation Influential analysis Nonparametric robust estimates Optimal bandwidth Robust control |
| description |
In this paper, under a semiparametric partly linear regression model with fixed design, we introduce a family of robust procedures to select the bandwidth parameter. The robust plug-in proposal is based on nonparametric robust estimates of the νth derivatives and under mild conditions, it converges to the optimal bandwidth. A robust cross-validation bandwidth is also considered and the performance of the different proposals is compared through a Monte Carlo study. We define an empirical influence measure for data-driven bandwidth selectors and, through it, we study the sensitivity of the data-driven bandwidth selectors. It appears that the robust selector compares favorably to its classical competitor, despite the need to select a pilot bandwidth when considering plug-in bandwidths. Moreover, the plug-in procedure seems to be less sensitive than the cross-validation in particular, when introducing several outliers. When combined with the three-step procedure proposed by Bianco and Boente [2004. Robust estimators in semiparametric partly linear regression models. J. Statist. Plann. Inference 122, 229-252] the robust selectors lead to robust data-driven estimates of both the regression function and the regression parameter. © 2007 Elsevier B.V. All rights reserved. |
| title |
Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis |
| title_short |
Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis |
| title_full |
Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis |
| title_fullStr |
Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis |
| title_full_unstemmed |
Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis |
| title_sort |
robust bandwidth selection in semiparametric partly linear regression models: monte carlo study and influential analysis |
| publishDate |
2008 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v52_n5_p2808_Boente http://hdl.handle.net/20.500.12110/paper_01679473_v52_n5_p2808_Boente |
| _version_ |
1768543366138560512 |