Multivariate cumulants in outlier detection for financial data analysis

Outlier Detection in Multivariate Data

The objective of this research is detection of outliers in multivariate data employing various distance measure, particularly using robust regression diagnosis technique. Several classical outlier identification methods are based on the sample mean and covariance matrix in general. But they do not always yield better result, as they themselves are affected by the outliers. Sometimes one outlier...

Multivariate outlier detection with compositional data

Multivariate outlier detection is usually based on Mahalanobis distances, by plugging in robust estimates of location and covariance. For compositional data, carrying only relative information, a special transformation needs to be consulted in order to be able to work in the appropriate geometry. The effect of the transformation is discussed in this contribution. Furthermore, different possibil...

A statistical test for outlier identification in data envelopment analysis

In the use of peer group data to assess individual, typical or best practice performance, the effective detection of outliers is critical for achieving useful results. In these ‘‘deterministic’’ frontier models, statistical theory is now mostly available. This paper deals with the statistical pared sample method and its capability of detecting outliers in data envelopment analysis. In the prese...

Finding Outlier DMUs in Data Envelopment Analysis

Multivariate Outlier Detection Using Independent Component Analysis

The recent developments by considering a rather unexpected application of the theory of Independent component analysis (ICA) found in outlier detection , data clustering and multivariate data visualization etc . Accurate identification of outliers plays an important role in statistical analysis. If classical statistical models are blindly applied to data containing outliers, the results can be ...