ROBUST SMOOTHED CANONICAL CORRELATION ANALYSIS FOR FUNCTIONAL DATA

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.