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

Jeuken J, Sijben A, Alenda C, Rijntjes J, Dekkers M, Boots-Sprenger S, McLendon R, Wesseling P: Robust detection of EGFR copy number changes and EGFR variant III: Technical aspects and relevance for glioma diagnostics. 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. About robust regression, robust estimators and statistical procedures, outlier detection, extreme value theory, data cleaning, outlier detection in high dimensional data, non parametric statistics. €� Most common regression methods (linear, logistic, etc.) • Time Series Modeling. €� Principal Component Analysis. 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]. Leroy, “Robust regression and outlier detection”, John Wiley &. €� Example of embedding graphics from S+/R. Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. Outlier identification was performed with regression analysis to detect data points at or beyond 95% confidence intervals for residuals. 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. Regression analysis identified outliers. 3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence).