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

Agglomerative Hierarchical Clustering. Modeling the Z-score Tuning Parameters for the Port Correlation Algorithm. This program has the ability to identify a certain percentage of outliers in each bootstrap sample. 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. We further extend the sparse regression algorithm to a robust sparse regression algorithm for outlier detection, which provides superior accuracy compared to the traditional IQR method. Table 2: Benchmark Results for Combinations of Subset Size and MCD Repetitions. Parameters of the regression models in the bootstrap procedure. Consequently, the literature on outliers is dispersed in statistics, process engineering and systems science as robust estimation, regression, system identification, and data analysis. Robust Regression and Outlier Detection. Robust Correlation as a Distance Metric. I encountered a wonderful survey article, "Robust statistics for outlier detection," by Peter Rousseeuw and Mia Hubert. Unfortunately, many statistics practitioners are not aware of the fact that the OLS method can be adversely affected by the existence of outliers. Step 4: Fit the LTS to the bootstrapped values b yi on the fixed X to obtain bˆ b. Table 3: Percentages of Categories of Events Discovered Using Port Clustering and Two-Stage. Table 4: Estimated Parameters for the Regression Model of Variance Correction Values. 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. As an alternative, a robust method was put .