Stor may www.econstor.eu The Maximum Asymptotic Bias of Outlier Identi ers Claudia Becker and Ursula Gather 1 Abstract In their paper, Davies and Gather 1993 formalized the task of outlier identi cation, considering also certain performance criteria for outlier identi ers. One of those criteria

A dense set of single nucleotide polymorphisms (SNP) covering the genome and an efficient method to assess SNP genotypes are expected to be available in the near future. An outstanding question is how to use these technologies efficiently to identify genes affecting liability to complex disorders

In this paper, the task of identifying outliers in exponential samples is treated conceptionally in the sense of Davies and Gather (1989, 1993) by means of a so-called outlier region. In case of an exponential distribution, an empirical approximation of such a region -- also called an outlier

This paper studies a new data mining problem called multiinstance outlier identification. This problem arises in tasks where each sample consists of many alternative feature vectors (instances) that describe it. This paper defines the multi-instance outliers and analyzes the basic types

For many KDD applications finding the outliers, i.e. the rare events, is more interesting and useful than finding the common cases, e.g. detecting criminal activities in E-commerce. Being an outlier, however, is not just a binary property. Instead, it

points, such as comparing error rate estimates, evaluating different discriminant rules, and studying outlier influence. The study may be of assistance to researchers who are interested in conducting Monte Carlo studies, especially in choosing the values of the parameters of factors under consideration

. We present an efficient convex optimization-based algorithm we call Outlier Pursuit, that under some mild assumptions on the uncorrupted points (satisfied, e.g., by the standard generative assumption in PCA problems) recovers the exact optimal low-dimensional subspace, and identifies the corrupted

Abstract—We consider the problem of outlier detection and interpretation. While most existing studies focus on the first problem, we simultaneously address the equally important challenge of outlier interpretation. We propose an algorithm that uncovers outliers in subspaces of reduced

algorithms that are applied using different set of features. Every outlier detection algorithm uses a small subset of features that are randomly selected from the original feature set. As a result, each outlier detector identifies different outliers, and thus assigns to all data records outlier scores

relevant in the context of frontier analysis since it will identify the ¯rms that exert the most in°uence on the resulting e±ciency scores.