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

Consequently, the literature on outliers is dispersed in statistics, process engineering and systems science as robust estimation, regression, system identification, and data analysis. 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. There are also methods for linear regression which are resistant to the presence of outliers, which fall into the category of robust regression. Whole host of other multivariate methods. That is the only positive aspect of the Lewandowsky research I've thus far been able to detect. Tuesday, 9 April 2013 at 13:07. I think that the Lewandowsky data set may have a chance of entering the robust regression textbooks. The supplementary online material for the article is being extended to contain additional information (e.g., the outlier analysis from the preceding post). 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. I encountered a wonderful survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. Robust Regression and Outlier Detection (Wiley Series in Probability and Statistics) book download.