Adaptive Bandwidth Choice for Kernel Regression
提出一种数据驱动的局部带宽选择方法，用于核估计回归函数在某点的值。该带宽估计量具有一致性和渐近正态性，模拟显示有限样本下表现良好，优于全局带宽估计量，并适用于局部线性回归和加权局部多项式拟合。
Abstract A data-based procedure is introduced for local bandwidth selection for kernel estimation of a regression function at a point. The estimated bandwidth is shown to be consistent and asymptotically normal as an estimator of the (asymptotic) optimal value for minimum mean square estimation. Simulation studies indicate satisfactory behavior of the new bandwidth estimator in finite samples. The findings are improvements over a global bandwidth estimator. The same methodology works for local linear regression and extends easily to weighted local polynomial fits.