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Abstract:

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.

Registro:

Documento: Artículo
Título:Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis
Autor:Boente, G.; Rodriguez, D.
Filiación:Departamento de Matemáticas, Instituto de Cálculo, FCEyN, Argentina
Palabras clave: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
Año:2008
Volumen:52
Número:5
Página de inicio:2808
Página de fin:2828
DOI: http://dx.doi.org/10.1016/j.csda.2007.10.017
Título revista:Computational Statistics and Data Analysis
Título revista abreviado:Comput. Stat. Data Anal.
ISSN:01679473
CODEN:CSDAD
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_01679473_v52_n5_p2808_Boente

Referencias:

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Citas:

---------- APA ----------
Boente, G. & Rodriguez, D. (2008) . Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis. Computational Statistics and Data Analysis, 52(5), 2808-2828.
http://dx.doi.org/10.1016/j.csda.2007.10.017
---------- CHICAGO ----------
Boente, G., Rodriguez, D. "Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis" . Computational Statistics and Data Analysis 52, no. 5 (2008) : 2808-2828.
http://dx.doi.org/10.1016/j.csda.2007.10.017
---------- MLA ----------
Boente, G., Rodriguez, D. "Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis" . Computational Statistics and Data Analysis, vol. 52, no. 5, 2008, pp. 2808-2828.
http://dx.doi.org/10.1016/j.csda.2007.10.017
---------- VANCOUVER ----------
Boente, G., Rodriguez, D. Robust bandwidth selection in semiparametric partly linear regression models: Monte Carlo study and influential analysis. Comput. Stat. Data Anal. 2008;52(5):2808-2828.
http://dx.doi.org/10.1016/j.csda.2007.10.017