标题： 平稳数据的核偏最小二乘法 显示英文标题

标题： Kernel partial least squares for stationary data

摘要： 我们研究了非参数回归中具有平稳相关数据的核偏最小二乘算法。在源条件和有效维数条件下，建立了核偏最小二乘估计量以真实回归函数为概率收敛速度。理论分析和模拟均表明，长程依赖会导致较慢的收敛速度。蛋白质动力学实例展示了核偏最小二乘具有很高的预测能力。 显示英文摘要

摘要： 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.