In this article we enunciate and rigorously demonstrate a new lemma which, based on a previously proposed theorem, proves the identifiability of leverage points in state estimation with specific reference to the least absolute value (LAV) estimator. In this context, we also propose an algorithm for the leverage point identification in LAV estimators whose performance is validated by means of extensive numerical simulations and compared against the well-known approach of projection statistics (PS). The obtained results confirm that the proposed method outperforms PS and represents a significant enhancement for LAV-based state estimators as it correctly identifies all the leverage points in the measurement set.

中文翻译：

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基于 LAV 的状态估计的杠杆点识别方法

在本文中，我们阐明并严格证明了一个新引理，该引理基于先前提出的定理，证明了状态估计中杠杆点的可识别性，具体参考最小绝对值 (LAV) 估计器。在这种情况下，我们还提出了一种用于 LAV 估计器中杠杆点识别的算法，其性能通过广泛的数值模拟得到验证，并与众所周知的投影统计 (PS) 方法进行比较。获得的结果证实，所提出的方法优于 PS，并且代表了基于 LAV 的状态估计器的显着增强，因为它正确识别了测量集中的所有杠杆点。