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Robust regression and outlier detection Annick M. Leroy, Peter J. Rousseeuw
Publisher: Wiley

They define outlier detection as the problem of “[] finding patterns in data that do not conform to expected normal behavior“. Leroy, “Robust regression and outlier detection”, John Wiley &. 3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence). Nassim Nicholas Taleb, among other people, has some considered criticisms of the least square linear regression, because of the un-stability (lack of robustness) of such from the action of the outliers. Tuesday, 9 April 2013 at 13:07. The first one, Outlier Detection: A Survey, is written by Chandola, Banerjee and Kumar. After an For example: neural networks, SVM, rule-based, clustering, nearest neighbors, regression, etc. The next time I perform My (uninformed) hunch is that robustness of the least squares linear regression is an underdeveloped topic in the literature - so picking a method to detect lack of robustness on cost/benefit is not informed by the literature. Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics) book download. Another useful survey article is “Robust statistics for outlier detection,” by Peter Rousseeuw and Mia Hubert.