Artículo
Robust inference in generalized partially linear models
Fecha de publicación:
12/2010
Editorial:
Elsevier Science
Revista:
Computational Statistics And Data Analysis
ISSN:
0167-9473
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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
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Articulos(IMAS)
Articulos de INSTITUTO DE INVESTIGACIONES MATEMATICAS "LUIS A. SANTALO"
Articulos de INSTITUTO DE INVESTIGACIONES MATEMATICAS "LUIS A. SANTALO"
Citación
Boente Boente, Graciela Lina; Rodriguez, Daniela Andrea; Robust inference in generalized partially linear models; Elsevier Science; Computational Statistics And Data Analysis; 54; 12; 12-2010; 2942-2966
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