Minimax prediction for functional linear regression with functional responses in reproducing kernel Hilbert spaces

作者：Lian Heng^*

来源：Journal of Multivariate Analysis, 2015, 140: 395-402.

DOI：10.1016/j.jmva.2015.06.005

• Functional data
• Functional response
• Minimax convergence rate
• Regularization

In this article, we consider convergence rates in functional linear regression with functional responses, where the linear coefficient lies in a reproducing kernel Hilbert space (RKHS). Without assuming that the reproducing kernel and the covariate covariance kernel are aligned, convergence rates in prediction risk are established. The corresponding lower bound in rates is derived by reducing to the scalar response case. Simulation studies and two benchmark datasets are used to illustrate that the proposed approach can significantly outperform the functional PCA approach in prediction.

• 出版日期2015-9