Abstract:
Robust nonparametric estimators for additive regression or autoregression models under an α-mixing condition are proposed. They are based on local M-estimators or local medians with kernel weights, and their asymptotic behaviour is studied. Moreover, these local M-estimators achieve the same univariate rate of convergence as their linear relatives.
Registro:
Documento: |
Artículo
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Título: | Robust kernel estimators for additive models with dependent observations |
Autor: | Bianco, A.; Boente, G. |
Filiación: | Instituto de Cálculo, Fac. Ciencias Exactas y Nat., Pabellón No. 2, Buenos Aires, 1428, Argentina Departamento de Matemáticas, Fac. Ciencias Exactas y Nat., Pabellón No. 1, Buenos Aires, 1428, Argentina
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Palabras clave: | Additive model; Kernel estimation; Nonparametric regression; Robust estimation; α-mixing conditions |
Año: | 1998
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Volumen: | 26
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Número: | 2
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Página de inicio: | 239
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Página de fin: | 255
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DOI: |
http://dx.doi.org/10.2307/3315508 |
Título revista: | Canadian Journal of Statistics
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Título revista abreviado: | Can. J. Stat.
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ISSN: | 03195724
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Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03195724_v26_n2_p239_Bianco |
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Citas:
---------- APA ----------
Bianco, A. & Boente, G.
(1998)
. Robust kernel estimators for additive models with dependent observations. Canadian Journal of Statistics, 26(2), 239-255.
http://dx.doi.org/10.2307/3315508---------- CHICAGO ----------
Bianco, A., Boente, G.
"Robust kernel estimators for additive models with dependent observations"
. Canadian Journal of Statistics 26, no. 2
(1998) : 239-255.
http://dx.doi.org/10.2307/3315508---------- MLA ----------
Bianco, A., Boente, G.
"Robust kernel estimators for additive models with dependent observations"
. Canadian Journal of Statistics, vol. 26, no. 2, 1998, pp. 239-255.
http://dx.doi.org/10.2307/3315508---------- VANCOUVER ----------
Bianco, A., Boente, G. Robust kernel estimators for additive models with dependent observations. Can. J. Stat. 1998;26(2):239-255.
http://dx.doi.org/10.2307/3315508