Nonparametric Regression: Optimal Local Bandwidth Choice
研究了回归函数的核估计，提出一种基于局部交叉验证准则的数据驱动方法来自适应选择带宽，该方法在局部二次误差度量下渐近最优，并通过蒙特卡洛实验和医学数据验证。
SUMMARY Kernel estimators of a regression function are investigated. The bandwidths are locally chosen by a data-driven method based on the minimization of a local cross-validation criterion. This method is shown to be asymptotically optimal with respect to local quadratic measures of errors. Monte Carlo experiments are presented, and finally the method is applied to some data of medical interest.