On Local Smoothing of Nonparametric Curve Estimators
本文在密度估计和回归中提出了交叉验证和平滑交叉验证的局部版本，通过平滑变化的带宽捕捉曲线的局部波动，方法准确、易用且计算高效。
Abstract We develop new local versions of familiar smoothing methods, such as cross-validation and smoothed cross-validation, in the contexts of density estimation and regression. These new methods are locally adaptive in the sense that they capture smooth local fluctuations in the curve by using smoothly varying bandwidths that change as the character of the curve changes. Moreover, the new methods are accurate, easy to apply, and computationally expedient.