A performance evaluation in multivariate outliers identification methods.

Abstract

Methodologies for identifying multivariate outliers are extremely important in statistical analysis. Outliers may reveal relevant information to variables under investigation. Statistical applications without prior identification of possible extreme values may yield controversial results and induce mistaken decision making. In many contexts, outliers are points of great practical interest. Given this, this paper seeks to discuss methodologies for the detection of multivariate outliers through a fair and adequate comparative technique in their simulation procedure. The comparison considers detection techniques based on Mahalanobis distance, besides a methodology based on cluster analysis technique. Sensitivity, specificity, and accuracy metrics are used to measure the method quality. An analysis of the computational time required to perform the procedures is evaluated. The technique based on cluster analysis revealed a noticeable superiority over the others in detection quality and also in execution time.