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Asymptotics of prediction in functional linear regression with functional outputs

Abstract : We study prediction in the functional linear model with functional outputs, Y = SX + ε, where the covari-ates X and Y belong to some functional space and S is a linear operator. We provide the asymptotic mean square prediction error for a random input with exact constants for our estimator which is based on the functional PCA of X. As a consequence we derive the optimal choice of the dimension k n of the projection space. The rates we obtain are optimal in minimax sense and generalize those found when the output is real. Our main results hold for class of inputs X(·) that may be either very irregular or very smooth. We also prove a central limit theorem for the predictor. We show that, due to the underlying inverse problem, the bare estimate cannot converge in distribution for the norm of the function space.
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https://hal.archives-ouvertes.fr/hal-01819423
Contributor : Christophe Crambes Connect in order to contact the contributor
Submitted on : Wednesday, June 20, 2018 - 2:33:10 PM
Last modification on : Thursday, October 13, 2022 - 8:22:09 AM
Long-term archiving on: : Tuesday, September 25, 2018 - 3:21:33 PM

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Christophe Crambes, André Mas. Asymptotics of prediction in functional linear regression with functional outputs. Bernoulli, 2013, 19 (5B), pp.2627 - 2651. ⟨10.3150/12-BEJ469⟩. ⟨hal-01819423⟩

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