Artículo

Boente, G.; Martínez, A.; Salibián-Barrera, M. "Robust estimators for additive models using backfitting" (2017) Journal of Nonparametric Statistics. 29(4):744-767
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Abstract:

Additive models provide an attractive setup to estimate regression functions in a nonparametric context. They provide a flexible and interpretable model, where each regression function depends only on a single explanatory variable and can be estimated at an optimal univariate rate. Most estimation procedures for these models are highly sensitive to the presence of even a small proportion of outliers in the data. In this paper, we show that a relatively simple robust version of the backfitting algorithm (consisting of using robust local polynomial smoothers) corresponds to the solution of a well-defined optimisation problem. This formulation allows us to find mild conditions to show Fisher consistency and to study the convergence of the algorithm. Our numerical experiments show that the resulting estimators have good robustness and efficiency properties. We illustrate the use of these estimators on a real data set where the robust fit reveals the presence of influential outliers. © 2017, © American Statistical Association and Taylor & Francis 2017.

Registro:

Documento: Artículo
Título:Robust estimators for additive models using backfitting
Autor:Boente, G.; Martínez, A.; Salibián-Barrera, M.
Filiación:Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires and IMAS, CONICET, Buenos Aires, Argentina
Department of Statistics, University of British Columbia, Vancouver, BC, Canada
Palabras clave:Fisher-consistency; kernel weights; robust estimation; smoothing
Año:2017
Volumen:29
Número:4
Página de inicio:744
Página de fin:767
DOI: http://dx.doi.org/10.1080/10485252.2017.1369077
Título revista:Journal of Nonparametric Statistics
Título revista abreviado:J. Nonparametric Stat.
ISSN:10485252
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_10485252_v29_n4_p744_Boente

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Citas:

---------- APA ----------
Boente, G., Martínez, A. & Salibián-Barrera, M. (2017) . Robust estimators for additive models using backfitting. Journal of Nonparametric Statistics, 29(4), 744-767.
http://dx.doi.org/10.1080/10485252.2017.1369077
---------- CHICAGO ----------
Boente, G., Martínez, A., Salibián-Barrera, M. "Robust estimators for additive models using backfitting" . Journal of Nonparametric Statistics 29, no. 4 (2017) : 744-767.
http://dx.doi.org/10.1080/10485252.2017.1369077
---------- MLA ----------
Boente, G., Martínez, A., Salibián-Barrera, M. "Robust estimators for additive models using backfitting" . Journal of Nonparametric Statistics, vol. 29, no. 4, 2017, pp. 744-767.
http://dx.doi.org/10.1080/10485252.2017.1369077
---------- VANCOUVER ----------
Boente, G., Martínez, A., Salibián-Barrera, M. Robust estimators for additive models using backfitting. J. Nonparametric Stat. 2017;29(4):744-767.
http://dx.doi.org/10.1080/10485252.2017.1369077