A Cross-Validatory Choice of Smoothing Parameter in Adaptive Location Estimation
提出一种基于交叉验证的数据驱动方法，用于选择核自适应位置估计中的平滑参数，模拟显示该方法在有限样本下表现良好且计算效率高。
Abstract This article proposes a new data-driven method for selecting the smoothing parameter involved in constructing kernel-based adaptive location estimators. The method consists of minimizing a cross-validatory criterion with respect to the bandwidth occurring in the kernel-type estimators of the efficient score function. It is shown that the location estimator with a data-driven bandwidth selector is indeed an adaptive estimator. A simulation study reveals that the method is also practicable, showing that our estimator performs well in comparison with some other well-known location estimators. It also shows that our method has comparable finite sample performance with the bootstrap method of selecting the smoothing parameter and yet has great computational advantages. Key Words: Adaptive estimatorCross-validationData-driven bandwidth selectorKernel estimator