A proposal for identifying multivariate outliers

Josino José Barbosa, Tiago Martins Pereira, Fernando Luiz Pereira de Oliveira


The identification of outliers plays an important role in the statistical analysis, since such observations may contain important information regarding the hypotheses of the study. If classical statistical models are blindly applied to data containing atypical values, the results may be misleading and mistaken decisions can be made. Moreover, in practical situations, the outliers themselves are often the special points of interest and their identification may be the main objective of the investigation. In this way, it was proposed to propose a technique of detection of multivariate outliers, based on cluster analysis and to compare this technique with the method of identification of outliers via Mahalanobis Distance. For data generation, Monte Carlo method simulation and the mixed multivariate normal distribution technique were used. The results presented in the simulations showed that the proposed method was superior to the Mahalanobis method for both sensitivity and specificity, that is, it presented greater ability to correctly diagnose outliers and non-outliers individuals. In addition, the proposed methodology was illustrated with an application in real data from the health area.


Power inverse Lindley distribution, Methods of estimation; Likelihood; Monte Carlo simulation


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DOI: https://doi.org/10.5902/2179460X29535

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