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

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. 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. Outliers: detection and robust estimation (RLM) Part 3: Outlook. Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. Robust Regression And Outlier Detection Wiley Series In Probability And Statistics (99.75) Robust Statistics: The Approach Based On Influence Functions. Robust Regression and Outlier Detection. Robust statistical methods can ensure that the results are not overly influenced by these outliers, either by reducing the effect of outlier datapoints (e.g., robust regression using iteratively reweighted least squares) or by separately Robust tools for fMRI group analysis are increasingly available, both as part of standard software packages (such as the “outlier detection” technique implemented in FSL: Woolrich, 2008) and as add-on toolboxes (Wager et al., 2005). Brief show case: quantile regression, non-parametric estimation The future of statistics in python. Properties of estimators and inference. Motion correction algorithms may create spurious brain activations in the absence of subject motion. The detection of outliers before analyzing the data analysis is not done then it may lead to model misspecification, biased parameter estimation and incorrect results. In fitting regression line outliers can significantly change the slope.