An attempt to analyse Iterative Data Snooping and L1-norm based on Monte Carlo simulation in the context of leveling networks

Abstract

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 , 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.