Outliers in bioequivalence trials may arise through various mechanisms, requiring different interpretation and handling of such data points. For example, regulatory authorities might permit exclusion from analysis of outliers caused by product or process failure, while exclusion of outliers caused by subject-by-treatment interaction generally is not acceptable. In standard 2 x 2 crossover studi...

Spatial outliers represent locations which are signiicantly diierent from their neighborhoods even though they may not be signiicantly diierent from the entire population. Identiication of spatial outliers can lead to the discovery of unexpected , interesting, and implicit knowledge, such as local instability. In this paper, we rst provide a general deenition of S-outliers for spatial outliers....

In many Knowledge Discovery applications, finding outliers is more interesting than finding inliers in a dataset. The perception of outliers is rare cases in dataset in which is being described as abnormal data in the information table. Outliers detections are applied in many important applications like fraud detection systems to uncover the suspicious objects which may have important knowledge...

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...

The ARTigo project is based on a social image tagging game developed at the Ludwig Maximilian University of Munich. It provides us with a large database of images described by tags which were chosen by the players of the ARTigo game. The outlying properties detection problem (ODP) defines the problem of finding explanations for the most outlying properties of a certain object. This work intends...

In previous research the support vector data description (SVDD) is proposed to solve the problem of one-class classification. In one-class classification, one set of data, called the target set, has to be distinguished from the rest of the feature space. In the original optimization of the support vector data description, two parameters have to be given beforehand by the user. In this paper a n...

We initiate the study of metric embeddings with outliers. Given some metric space (X, ρ) we wish to find a small set of outlier points K ⊂ X and either an isometric or a low-distortion embedding of (X \K, ρ) into some target metric space. This is a natural problem that captures scenarios where a small fraction of points in the input corresponds to noise. For the case of isometric embeddings we ...

We develop exact and approximate algorithms for computing optimal separators and measuring the extent to which two point sets in d-dimensional space are separated, with respect to different classes of separators and various extent measures. This class of geometric problems generalizes two widely studied problem families, namely separability and the computation of statistical estimators.

DETECTION OF OUTLIERS IN TIME SERIES DATA Samson Kiware, B.A. Marquette University, 2010 This thesis presents the detection of time series outliers. The data set used in this work is provided by the GasDay Project at Marquette University, which produces mathematical models to predict the consumption of natural gas for Local Distribution Companies (LDCs). Flow with no outliers is required to dev...