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This work studies outlier detection and robust estimation with data that are naturally distributed into groups and which followapproximately a linear regression model with fixed group effects. For this, several methods are considered. First, the robust fitting method of Pe a andYohai [A fast procedure for outlier diagnostics in large regression problems. JAm Stat Assoc. 1999;94:434-445], called principal sensitivity components (PSC) method, is adapted to the grouped data structure and the mentioned model. The robust methods RDL1 of Hubert and Rousseeuw [Robust regression with both continuous and binary regressors. J Stat Plan Inference. 1997;57:153-163] and M-S of Maronna and Yohai [Robust regression with both continuous and categorical predictors. Journal of Statistical Planning and Inference 2000;89:197-214] are also considered. These three methods are compared in terms of their effectiveness in outlier detection and their robustness through simulations, considering several contamination scenarios and growing contamination levels. Results indicate that the adapted PSC procedure is able to detect a high percentage of true outliers and a small number of false outliers. It is appropriate when the contamination is in the error term or in the covariates, detecting also possibly masked high leverage points. Moreover, in simulations the final robust regression estimator preserved good efficiency under Normality while keeping good robustness properties.
linear models with fixed effects; masking effect; outlier detection; principal sensitivity components; robust estimation; diagnostics