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

Some statistics are more robust than others to data contamination. Here is a plot of post 96 observations . Leroy, “Robust regression and outlier detection”, John Wiley &. Robust Regression and Outlier Detection. 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. Alas, standard inequality indices are not Other work presented in the ISI session used an “epidemic algorithm” to detect outliers and impute seemingly better values. Even if you exclude CCSM4 as an outlier the contribution to the trend since '96 from volcanic recovery in the model ensemble is about the same as the observed trend. This method simulates an epidemic in If reliable data are available on covariates of incomes from the same survey then one could use a regression-adjustment, focusing instead on the 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. 3 The initial level of income per capita is a robust and significant variable for growth (in terms of conditional or beta convergence). The volcanic term is critical here – at the moment it looks to me as though the ENSO term is robust, and the solar fairly robust (and small) once a response function is included.