On Optimal Data-Based Bandwidth Selection in Kernel Density Estimation
提出一种核密度估计的带宽选择方法，通过将估计值代入最优带宽的渐近表达式并做两项重要修改，实现了极快的渐近收敛速度n^{-1/2}，并与其他方法进行了比较和小样本表现分析。
A bandwidth selection method is proposed for kernel density estimation. This is based on the straightforward idea of plugging estimates into the usual asymptotic representation for the optimal bandwidth, but with two important modifications. The result is a bandwidth selector with the, by nonparametric standards, extremely fast asymptotic rate of convergence of n−½ where n ↑ ∞ denotes sample size. Comparison is given to other bandwidth selection methods, and small sample impact is investigated.