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Abstract:

In many situations, data follow a generalized partly linear model in which the mean of the responses is modeled, through a link function, linearly on some covariates and nonparametrically on the remaining ones. A new class of robust estimates for the smooth function η, associated to the nonparametric component, and for the parameter β, related to the linear one, is defined. The robust estimators are based on a three-step procedure, where large values of the deviance or Pearson residuals are bounded through a score function. These estimators allow us to make easier inferences on the regression parameter β and also improve computationally those based on a robust profile likelihood approach. The resulting estimates of β turn out to be root-n consistent and asymptotically normally distributed. Besides, the empirical influence function allows us to study the sensitivity of the estimators to anomalous observations. A robust Wald test for the regression parameter is also provided. Through a Monte Carlo study, the performance of the robust estimators and the robust Wald test is compared with that of the classical ones. © 2010 Elsevier B.V. All rights reserved.

Registro:

Documento: Artículo
Título:Robust inference in generalized partially linear models
Autor:Boente, G.; Rodriguez, D.
Filiación:Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina CONICET, Argentina
Instituto de Cálculo, FCEyN, Ciudad Universitaria, Pabellón 2, Buenos Aires, C1428EHA, Argentina
Palabras clave:Asymptotic properties; Generalized partly linear models; Rate of convergence; Robust estimation; Smoothing techniques; Tests; Normal distribution; Testing; Asymptotic properties; Partly linear; Rate of convergence; Robust estimation; Smoothing techniques; Parameter estimation
Año:2010
Volumen:54
Número:12
Página de inicio:2942
Página de fin:2966
DOI: http://dx.doi.org/10.1016/j.csda.2010.05.025
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_v54_n12_p2942_Boente

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Citas:

---------- APA ----------
Boente, G. & Rodriguez, D. (2010) . Robust inference in generalized partially linear models. Computational Statistics and Data Analysis, 54(12), 2942-2966.
http://dx.doi.org/10.1016/j.csda.2010.05.025
---------- CHICAGO ----------
Boente, G., Rodriguez, D. "Robust inference in generalized partially linear models" . Computational Statistics and Data Analysis 54, no. 12 (2010) : 2942-2966.
http://dx.doi.org/10.1016/j.csda.2010.05.025
---------- MLA ----------
Boente, G., Rodriguez, D. "Robust inference in generalized partially linear models" . Computational Statistics and Data Analysis, vol. 54, no. 12, 2010, pp. 2942-2966.
http://dx.doi.org/10.1016/j.csda.2010.05.025
---------- VANCOUVER ----------
Boente, G., Rodriguez, D. Robust inference in generalized partially linear models. Comput. Stat. Data Anal. 2010;54(12):2942-2966.
http://dx.doi.org/10.1016/j.csda.2010.05.025