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

Robust Correlation as a Distance Metric. As an alternative, a robust method was put . Table 2: Benchmark Results for Combinations of Subset Size and MCD Repetitions. Leroy, “Robust regression and outlier detection”, John Wiley &. Agglomerative Hierarchical Clustering. This program has the ability to identify a certain percentage of outliers in each bootstrap sample. Modeling the Z-score Tuning Parameters for the Port Correlation Algorithm. 3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence). Parameters of the regression models in the bootstrap procedure. A different type of approach is to formulate the detection of differential splicing as an outlier detection problem, as in REAP (Regression-based Exon Array Protocol) or FIRMA (Finding Isoforms using Robust Multichip Analysis) [15,16]. Unfortunately, many statistics practitioners are not aware of the fact that the OLS method can be adversely affected by the existence of outliers. To attest that our results were not biased due to statistical outliers, we next performed robust regression analyses using the same explanatory variables. Step 4: Fit the LTS to the bootstrapped values b yi on the fixed X to obtain bˆ b. Robust statistical methods can ensure that the results are not overly influenced by these outliers, either by reducing the effect of outlier datapoints (e.g., robust regression using iteratively reweighted least squares) or by separately Robust tools for fMRI group analysis are increasingly available, both as part of standard software packages (such as the “outlier detection” technique implemented in FSL: Woolrich, 2008) and as add-on toolboxes (Wager et al., 2005). Table 4: Estimated Parameters for the Regression Model of Variance Correction Values. Table 3: Percentages of Categories of Events Discovered Using Port Clustering and Two-Stage.