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

Unfortunately, many statistics practitioners are not aware of the fact that the OLS method can be adversely affected by the existence of outliers. Robust Regression and Outlier Detection Average Reviews: (More customer reviews)These authors provide an excellent guide to the available theory of robust regression. Leroy · Tweetear Book Details: Book Title: Robust Regression and Outlier Detection Author: Peter J. €� Principal Component Analysis. Step 4: Fit the LTS to the bootstrapped values b yi on the fixed X to obtain bˆ b. €� Example of embedding graphics from S+/R. This program has the ability to identify a certain percentage of outliers in each bootstrap sample. Robust Regression and Outlier Detection by Peter J. As an alternative, a robust method was put . To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables. Parameters of the regression models in the bootstrap procedure. €� Most common regression methods (linear, logistic, etc.) • Time Series Modeling.