Reduced rank modeling for functional regression with functional responses
The paper under review deals with regression problems where both the predictor and the response are functional in nature. The authors consider functional reduced rank regression in the framework of reproducing kernel Hilbert spaces, which can be formulated in the form of linear factor regression with estimated multivariate factors, and achieves dimension reduction in both the predictor and the response spaces. The model is more parsimonious and allows interpreting the estimated coefficients more easily than in general functional linear regression models. The paper is organized as follows. In section 2, the authors discuss the estimation approach of the functional reduced rank regression model and obtain the convergence rate of the estimator. They further discuss the effect of discretization in implementation and theory. In section 3 simulation studies and real data illustrations are contained. The paper is concluded with a discussion in section 4.
scientific article; zbMATH DE number 6997753
scientific article