Artículo
Robust estimators in semi-functional partial linear regression models
Fecha de publicación:
02/2017
Editorial:
Elsevier Inc
Revista:
Journal Of Multivariate Analysis
ISSN:
0047-259X
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Partial linear models have been adapted to deal with functional covariates to capture both the advantages of a semi-linear modelling and those of nonparametric modelling for functional data. It is easy to see that the estimation procedures for these models are highly sensitive to the presence of even a small proportion of outliers in the data. To solve the problem of atypical observations when the covariates of the nonparametric component are functional, robust estimates for the regression parameter and regression operator are introduced. Consistency results of the robust estimators and the asymptotic distribution of the regression parameter estimator are studied. The reported numerical experiments show that the resulting estimators have good robustness properties. The benefits of considering robust estimators is also illustrated on a real data set where the robust fit reveals the presence of influential outliers.
Palabras clave:
Functional Data
,
Kernel Smoothers
,
Partial Linear Models
,
Robust Estimation
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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; Vahnovan, Alejandra Valeria; Robust estimators in semi-functional partial linear regression models; Elsevier Inc; Journal Of Multivariate Analysis; 154; 2-2017; 59-84
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