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

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. Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). The implemented statistical method will be based on robust PCA [3], which decomposes a given data matrix into a low-rank component and a sparse component containing the outlier elements. Mahwah, NJ: Applied regression analysis (2nd ed.). New York: How to detect and handle outliers. 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. Milwaukee Robust regression and outlier detection. What is new is that MathWorks addded a wide set of support functions that simplify common analysis tasks like plotting, outlier detection, generating predictions, performing stepwise regression, applying robust regression. Structure Discovery in Nonparametric Regression through Compositional Kernel Search - implementation - · Nuit Blanche in Review (May 2013) · How does the Rice one pixel camera work ? Robust PCA will be used to detect the atypical genomic markers, which have been .