Robust bootstrap methods with an application to geolocation in harsh LOS/NLOS environments
Robust bootstrap methods with an application to geolocation in harsh LOS/NLOS environments
复制标题
鲁棒引导方法及其在恶劣 LOS/NLOS 环境中地理定位的应用
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
A. Zoubir
中科院分区：
文献类型：
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作者：
Stefan Vlaski;Michael Muma;A. Zoubir
Bootstrap 是一种强大的统计推断计算工具，允许对估计值的分布进行估计，而无需对基础数据进行分布假设、依赖渐近结果或理论推导。另一方面，无论基础估计器的鲁棒性如何，在存在异常值的情况下，引导程序的鲁棒性特性都非常差。这激发了增强引导程序本身的需要。建议对两种现有的鲁棒引导方法进行改进，并引入一种用于鲁棒引导的新方法。在模拟研究中对这些方法进行了比较，并将所提出的方法应用于鲁棒地理定位。
The bootstrap is a powerful computational tool for statistical inference that allows for the estimation of the distribution of an estimate without distributional assumptions on the underlying data, reliance on asymptotic results or theoretical derivations. On the other hand, robustness properties of the bootstrap in the presence of outliers are very poor, irrespective of the robustness of the underlying estimator. This motivates the need to robustify the bootstrap procedure itself. Improvements to two existing robust bootstrap methods are suggested and a novel approach for robustifying the bootstrap is introduced. The methods are compared in a simulation study and the proposed method is applied to robust geolocation.