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

RobustWLS - Robust regression using Weighted-least-squares SPM2 SPM5 SPM8. Like covMcd, and robust fitting procedures like lmrob and glmrob for linear models and generalized linear models (specifically, a robust logistic regression procedure for binomial data, and a robust Poisson regression procedure for count data), among others. Summary: This toolbox implements an approach to detect New version has improved plotting and outlier detection. Table 3: Percentages of Categories of Events Discovered Using Port Clustering and Two-Stage. 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. There are also methods for linear regression which are resistant to the presence of outliers, which fall into the category of robust regression. The CCA-fMRI toolbox provides its own user interface and can .. "Robust Regression and Outlier Detection" states "robustregression . Robust Correlation as a Distance Metric. An adjusted boxplot rule, based on the upper and lower quartiles, along with a robust skewness estimator called the medcouple. Tries to devise estimators that are not so strongly affected by outliers. Table 4: Estimated Parameters for the Regression Model of Variance Correction Values. I encountered a wonderful survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. While this rule is appropriate for symmetric, approximately Gaussian data distributions, highly asymmetric situations call for an outlier detection rule that treats upward-outliers and downward-outliers differently. Author: Joern Diedrichsen & Reza Shadmehr. Agglomerative Hierarchical Clustering. Table 2: Benchmark Results for Combinations of Subset Size and MCD Repetitions. Modeling the Z-score Tuning Parameters for the Port Correlation Algorithm.