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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Autores principales: Boente, G., Rodriguez, D.
Formato: JOUR
Materias:
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
Acceso en línea:http://hdl.handle.net/20.500.12110/paper_01679473_v52_n5_p2808_Boente
Aporte de:
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires
id todo:paper_01679473_v52_n5_p2808_Boente
record_format dspace
spelling todo:paper_01679473_v52_n5_p2808_Boente2023-10-03T15:05:36Z Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis Boente, G. Rodriguez, D. 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. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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
Boente, G.
Rodriguez, D.
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.
format JOUR
author Boente, G.
Rodriguez, D.
author_facet Boente, G.
Rodriguez, D.
author_sort Boente, G.
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
url http://hdl.handle.net/20.500.12110/paper_01679473_v52_n5_p2808_Boente
work_keys_str_mv AT boenteg robustbandwidthselectioninsemiparametricpartlylinearregressionmodelsmontecarlostudyandinfluentialanalysis
AT rodriguezd robustbandwidthselectioninsemiparametricpartlylinearregressionmodelsmontecarlostudyandinfluentialanalysis
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