Marco Singer, Tatyana Krivobokova, Axel Munk.
Year: 2017, Volume: 18, Issue: 123, Pages: 1−41
We consider the kernel partial least squares algorithm for non- parametric regression with stationary dependent data. Probabilistic convergence rates of the kernel partial least squares estimator to the true regression function are established under a source and an effective dimensionality condition. It is shown both theoretically and in simulations that long range dependence results in slower convergence rates. A protein dynamics example shows high predictive power of kernel partial least squares.