Title: Kernel partial least squares for stationary data,
Abstract: 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.