In their paper, Davies and Gather (1993) formalized the task of outlier identiica-tion, considering also certain performance criteria for outlier identiiers. One of those criteria, the maximum asymptotic bias, is carried over here to multivariate outlier identiiers. We show how this term depends on the respective biases of estimators which are used to construct the identiier. It turns out that ...

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 ...

Clustering is an extremely important task in a wide variety of application domains especially in management and social science research. In this paper, an iterative procedure of clustering method based on multivariate outlier detection was proposed by using the famous Mahalanobis distance. At first, Mahalanobis distance should be calculated for the entire sample, then using T -statistic fix a U...

Outlier detection is a primary step in many data-mining applications. We present several methods for outlier detection, while distinguishing between univariate vs. multivariate techniques and parametric vs. nonparametric procedures. In presence of outliers, special attention should be taken to assure the robustness of the used estimators. Outlier detection for data mining is often based on dist...

A fundamental challenge for researchers studying the brain is to explain how distributed patterns of brain activity relate to a specific representation or computation. Multivariate techniques are therefore becoming increasingly popular for pattern localization of functional magnetic resonance imaging (fMRI) data. The increased power of these techniques can be offset by their susceptibility to m...

First of all we would like to thank the editor, Professor Andrea Cerioli, for inviting us to submit our work and for requesting comments from some esteemed colleagues. We were surprised by the number of invited comments and grateful to their contributing authors, all of whom raised important points and/or offered valuable suggestions. We are happy for the opportunity to rejoin the discussion. R...

A new method for multivariate outlier detection able to distinguish between extreme values of a normal distribution and values originating from a different distribution (outliers) is presented. To facilitate visualising multivariate outliers spatially on a map, the multivariate outlier plot, is introduced. In this plot different symbols refer to a distance measure from the centre of the distrib...

Wireless sensor networks have been a very important means in forest monitoring applications. A clustered network comprises set of cluster members and one head. The are normally located close to each other, with overlaps among their sensing coverage within the cluster. concurrently detect same event send Cluster Head node. This is where data aggregation deployed remove redundant at cost accuracy...