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

Often, however, a transformation will not eliminate or attenuate the leverage of influential outliers that bias the prediction and distort the significance of parameter estimates. Modeling the Z-score Tuning Parameters for the Port Correlation Algorithm. Table 2: Benchmark Results for Combinations of Subset Size and MCD Repetitions. Table 3: Percentages of Categories of Events Discovered Using Port Clustering and Two-Stage. 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. There are also methods for linear regression which are resistant to the presence of outliers, which fall into the category of robust regression. Robust Correlation as a Distance Metric. Leroy, “Robust regression and outlier detection”, John Wiley &. 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. 3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence). Table 4: Estimated Parameters for the Regression Model of Variance Correction Values. Agglomerative Hierarchical Clustering.