Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models
本文定义了与Huber-skip和最小修剪平方估计相关的几种异常值检测算法，回顾了其渐近理论，并分析了误检率（gauge）的渐近正态和泊松性质。
Abstract Outlier detection algorithms are intimately connected with robust statistics that down‐weight some observations to zero. We define a number of outlier detection algorithms related to the Huber‐skip and least trimmed squares estimators, including the one‐step Huber‐skip estimator and the forward search. Next, we review a recently developed asymptotic theory of these. Finally, we analyse the gauge, the fraction of wrongly detected outliers, for a number of outlier detection algorithms and establish an asymptotic normal and a Poisson theory for the gauge.