Abstract
The Interpretable Components (IC) use restrictions in order to have a better interpretation of the coefficients related to a Principal Component (PC). The efficiency of a (IC) due to a (PC) is made in relation to the angle formed between the Components, it being desirable the lowest value. In this context, an alternative to enrich this validation is the use of measures and circular distances so far not applied for this purpose. Given this motivation, this paper aims to propose the use of these measures to evaluate the robustness of the (IC) compared to samples contaminated with outliers, using measures and circular distances. According to various scenarios evaluated through Monte Carlo simulation, it was concluded that the use of these measures are recommended to validate the (PC) and when considering the Toeplitz correlation structure, IC were more robust in relation to the presence of outliers.
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