In extending univariate outlier detection methods to higher dimension, various special issues arise, such as limitations of visualization methods, inadequacy of marginal methods, lack of a natural order, limited scope of parametric modeling, and restriction to ellipsoidal contours when using Mahalanobis distance methods. Here we pass beyond these limitations via an approach based on depth funct...

Outlier analysis techniques are extensively used in many domains such as intrusion detection. Today, even with the most advanced statistical learning techniques, human judgment still plays an important role in outlier analysis tasks due to the difficulty of defining and collecting outlier examples. This work seeks to tackle this problem by introducing a new visualization design, ‘‘Z-Glyph,’’ a ...

We consider the problem of identifying examinees with aberrant response patterns in a computerized adaptive test (CAT). The vector of responses yi of person i from the CAT comprise a multivariate response vector. Multivariate observations may be outlying in many di erent directions and we characterize speci c directions as corresponding to outliers with di erent interpretations. We develop a cl...

In extending univariate outlier detection methods to higher dimension, various issues arise: limited visualization methods, inadequacy of marginal methods, lack of a natural order, limited parametric modeling, and, when using Mahalanobis distance, restriction to ellipsoidal contours. To address and overcome such limitations, we introduce nonparametric multivariate outlier identifiers based on m...

This paper presents an application of a local density based outlier detection method in compliance in the context of public health service management. Public health systems have consumed a significant portion of many governments’ expenditure. Thus, it is important to ensure the money is spent appropriately. In this research, we studied the potentials of applying an outlier detection method to m...