Comparison of Data-Driven Bandwidth Selectors
比较了最小二乘交叉验证、有偏交叉验证和插入法三种数据驱动带宽选择方法，通过渐近收敛速度和模拟研究发现插入法在密度足够平滑时效率最高，但鲁棒性不足。
Abstract This article compares several promising data-driven methods for selecting the bandwidth of a kernel density estimator. The methods compared are least squares cross-validation, biased cross-validation, and a plug-in rule. The comparison is done by asymptotic rate of convergence to the optimum and a simulation study. It is seen that the plug-in bandwidth is usually most efficient when the underlying density is sufficiently smooth, but is less robust when there is not enough smoothness present. We believe the plug-in rule is the best of those currently available, but there is still room for improvement. Key Words: Cross-validationData-driven bandwidth selectionDensity estimationKernel estimatorsPlug-in method