Functional Partial Least-Squares: Optimal Rates and Adaptation

Andrii Babii, Marine Carrasco and Idriss Tsafack

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Abstract: We consider the functional linear regression model with a scalar response and a Hilbert space-valued predictor, a well-known ill-posed inverse problem. We propose a new formulation of the functional partial least-squares (PLS) estimator related to the conjugate gradient method. We shall show that the estimator achieves the (nearly) optimal convergence rate on a class of ellipsoids and we introduce an early stopping rule which adapts to the unknown degree of ill-posedness. Some theoretical and simulation comparison between the estimator and the principal component regression estimator is provided.

Date: 2024-02
New Economics Papers: this item is included in nep-ecm
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