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"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.",
"authors": [
"Marco Singer",
"Tatyana Krivobokova",
"Axel Munk"
],
"id": "17-306",
"issue": 123,
"pages": [
1,
41
],
"title": "Kernel Partial Least Squares for Stationary Data",