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In this paper, we propose robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by combining sieves and robust association measures, leading to Fisher-consistent estimators for appropriate choices of the association measure. Under regularity conditions, the resulting estimators are consistent. The robust procedure allows us to construct detection rules to identify possible influential observations. The finite sample performance is illustrated through a simulation study in which contaminated data is included. The benefits of considering robust estimators are also illustrated on a real data set where the detection methods reveal the presence of influential observations for the first canonical directions that would be missed otherwise. © 2018 Elsevier Inc.


Documento: Artículo
Título:Robust sieve estimators for functional canonical correlation analysis
Autor:Alvarez, A.; Boente, G.; Kudraszow, N.
Filiación:Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Argentina
CONICET, Argentina
Facultad de Ciencias Exactas, Universidad Nacional de La Plata, Argentina
Palabras clave:Canonical correlation; Fisher-consistency; Functional data; Robust estimation; Sieves
Página de inicio:46
Página de fin:62
Título revista:Journal of Multivariate Analysis
Título revista abreviado:J. Multivariate Anal.


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---------- APA ----------
Alvarez, A., Boente, G. & Kudraszow, N. (2019) . Robust sieve estimators for functional canonical correlation analysis. Journal of Multivariate Analysis, 170, 46-62.
---------- CHICAGO ----------
Alvarez, A., Boente, G., Kudraszow, N. "Robust sieve estimators for functional canonical correlation analysis" . Journal of Multivariate Analysis 170 (2019) : 46-62.
---------- MLA ----------
Alvarez, A., Boente, G., Kudraszow, N. "Robust sieve estimators for functional canonical correlation analysis" . Journal of Multivariate Analysis, vol. 170, 2019, pp. 46-62.
---------- VANCOUVER ----------
Alvarez, A., Boente, G., Kudraszow, N. Robust sieve estimators for functional canonical correlation analysis. J. Multivariate Anal. 2019;170:46-62.