Learning rates for partially linear functional models with high dimensional scalar covariates

Abstract

This paper is concerned with learning rates for partial linear functional models (PLFM) within reproducing kernel Hilbert spaces (RKHS), where all the covariates consist of two parts: functional-type covariates and scalar ones. As opposed to frequently used functional principal component analysis for functional models, the finite number of basis functions in the proposed approach can be generated automatically by taking advantage of reproducing property of RKHS. This avoids additional computational costs on PCA decomposition and the choice of the number of principal components. Moreover, the coefficient estimators with bounded covariates converge to the true coefficients with linear rates, as if the functional term in PLFM has no effect on the linear part. In contrast, the prediction error for the functional estimator is significantly affected by the ambient dimension of the scalar covariates. Finally, we develop the proposed numerical algorithm for the proposed penalized approach, and some simulated experiments are implemented to support our theoretical results.