On the performance of principal component analysis in multiple gross erroridentification

In this paper, the use of the principal component test for the identificati on stage of three existing collective compensation strategies is presented. The three modified techniques are UBET (unbiased estimation of gross error s), SEGE (simultaneous estimation of gross errors) in the form of their rec ent modifications (MUBET and MSEGE), and SICC (serial identification with c ollective compensation). These techniques are modified to apply a statistic al test based on principal component analysis instead of the nodal, global, and measurement tests they use. The performance of the modified techniques is assessed by means of Monte Carlo simulations. Comparative analysis indi cates that PCA tests do not significantly enhance the ability in identifica tion features of these strategies, and even in some cases, it may lower the exact identification performance.