The goal of this paper is to evaluate the outlier identification performance of iterative Data Snooping (IDS) and L₁-norm in levelling networks by considering the redundancy of the network, number and size of the outliers. For this purpose, several Monte-Carlo experiments were conducted into three different levelling networks configurations. In addition, a new way to compare the results of IDS based on Least Squares (LS) residuals and robust estimators such as the L₁-norm has also been developed and presented. From the perspective of analysis only according to the success rate, it is shown that L₁-norm performs better than IDS for the case of networks with low redundancy $(\overline{r}<0.5)$, especially for cases where more than one outlier is present in the dataset. In the relationship between false positive rate and outlier identification success rate, however, IDS performs better than L₁-norm, independently of the levelling network configuration, number and size of outliers.

本文的目的是通过考虑网络的冗余、异常值的数量和大小来评估迭代数据侦听 (IDS) 和 L ₁范数在调平网络中的异常值识别性能。为此，在三种不同的调平网络配置中进行了几次蒙特卡罗实验。此外，还开发并提出了一种基于最小二乘 (LS) 残差和鲁棒估计量（例如 L ₁范数）比较 IDS 结果的新方法。从仅根据成功率分析的角度来看，在低冗余网络的情况下，L ₁ -norm 的性能优于 IDS$(\overline{r}<0.5)$，尤其是对于数据集中存在多个异常值的情况。然而，在误报率和异常值识别成功率之间的关系中，IDS 的表现优于 L ₁ -norm，与调平网络配置、异常值的数量和大小无关。