Identification of multivariate outliers – problems and challenges of visualization methods

The identification of outliers is often thought of as a means to eliminate observations from a data set to avoid disturbance in further analyses. But outliers may as well be the interesting observations in themselves, because they can give us hints about certain structures in the data or about special events during the sampling period. Therefore, appropriate methods for the detection of outliers are needed. Literature is abundant with procedures for detection and testing of single outliers in sample data. The difficulty of detection increases with the number of outliers and the dimension of the data because the outliers can be extreme in any growing number of directions. An overview of multivariate outlier detection methods that are provided in this study because of its growing importance in a wide variety of practical situations. We focus on methods that can be visually presented.

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