Spatially Smoothed Robust Covariance Estimation for Local Outlier Detection
提出一种考虑观测空间依赖性的异常点检测方法，通过平衡全局与局部协方差估计来识别异常，适用于气象站等空间数据，并给出了理论性质和高效算法。
Most multivariate outlier detection procedures ignore the spatial dependency of observations, which is present in many real datasets from various application areas.This article introduces a new outlier detection method that accounts for a (continuously) varying covariance structure, depending on the spatial neighborhood of the observations.The underlying estimator thus constitutes a compromise between a unified global covariance estimation, and local covariances estimated for individual neighborhoods.Theoretical properties of the estimator are presented, in particular related to robustness properties, and an efficient algorithm for its computation is introduced.The performance of the method is evaluated and compared based on simulated data and for a dataset recorded from Austrian weather stations.