Kernel partial least squares for stationary data
Statistics Theory
2017-06-13 v1 Statistics Theory
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.
Cite
@article{arxiv.1706.03559,
title = {Kernel partial least squares for stationary data},
author = {Marco Singer and Tatyana Krivobokova and Axel Munk},
journal= {arXiv preprint arXiv:1706.03559},
year = {2017}
}