Definition and Comparison of Robust Nonlinear Data Smoothing Algorithms
本文定义了一类基于运行中位数的非线性平滑器，用于处理含长尾或尖峰噪声的数据，并描述了其性能比较方法，揭示出具有优良低通特性、吉布斯回弹可忽略且抗非高斯干扰的平滑器。
Nonlinear data smoothers provide a practical method of finding smooth traces for data confounded with possibly long-tailed or occasionally spikey noise. They are resistant to the effects of extreme observations that are not part of the local pattern, yet they are able to respond rapidly to well-supported patterns. This article defines a collection of nonlinear smoothers based upon running medians and presents methods for describing and comparing their performance. The characterizations of the smoothers presented here reveal some with excellent low-pass transfer characteristics, negligible Gibbs rebound, and resistance to the effects of non-Gaussian disturbances.