Journal
STATISTICA SINICA
Volume 32, Issue 3, Pages 1269-1293Publisher
STATISTICA SINICA
DOI: 10.5705/ss.202020.0084
Keywords
Canonical correlation analysis; functional data; robust estimation; smoothing techniques
Categories
Funding
- ANPCYT [PICT 2018-00740]
- Universidad de Buenos Aires at Buenos Aires, Argentina (Graciela Boente) [20020170100022BA]
- Ministry of Economy and Com-petitiveness, Spain [MTM2016-76969P]
- Universidad Nacional de La Plata, Argentina (Nadia Kudraszow) [PPID x 030, PID I 231]
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This paper provides robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces using robust association and scale measures, combined with basis expansions and/or penalizations as a regularization tool. The resulting estimators are consistent under regularity conditions. The simulation study shows that the proposed robust method outperforms the existing classical procedure when the data are contaminated. A real data example is also presented.
We provide robust estimators for the first canonical correlation and directions of random elements on Hilbert separable spaces by using robust association and scale measures, combined with basis expansions and/or penalizations as a regularization tool. Under regularity conditions, the resulting estimators are consistent. The finite-sample performance of our proposal is illustrated by means of a simulation study that shows that, as expected, the robust method outperforms the existing classical procedure when the data are contaminated. A real data example is also presented.
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