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

Summary: The CCA-fMRI utilizes canonical correlation analysis in combination with the Balloon model and adaptive filtering of fMRI data to detect areas of brain activation. Outliers: detection and robust estimation (RLM) Part 3: Outlook. Mahwah, NJ: Applied regression analysis (2nd ed.). New York: How to detect and handle outliers. Author: Joern Diedrichsen & Reza Shadmehr. 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). Milwaukee Robust regression and outlier detection. The CCA-fMRI toolbox provides its own user interface and can .. RobustWLS - Robust regression using Weighted-least-squares SPM2 SPM5 SPM8. Brief show case: quantile regression, non-parametric estimation The future of statistics in python. Summary: This toolbox implements an approach to detect New version has improved plotting and outlier detection. Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Properties of estimators and inference.