随机切尾均值及其自举的统计分析

摘要

In this paper, we consider studentized randomly trimmed means and their boot-strap under relaxed conditions. By using empirical process approach of CG, we obtain the stu-densized central limit theorem of randomly trimmed means whose asymptotic properties do not depend on the underlying density under relaxed conditions, which extend the results of randomly trimmed means by Chen and Gine [1].%本文研究了松弛条件下学习随机切尾均值及其自举的统计性质。利用CG的经验过程方法，本文证明了在松弛条件下，学习随机切尾均值的中心极限定理，其渐进性质不依赖密度函数。进一步，得到了该性质对于学习切尾均值的自举依然成立，推广了Chen和Gine[1]随机切尾均值的相关研究结果。