Fast and robust estimators of variance components in the nested error model

Usual fitting methods for the nested error linearregression model are known to be very sensitive to theeffect of even a single outlier. Robust approaches for theunbalanced nested error model with proved robustness andefficiency properties, such as M-estimators, are typicallyobtained through iterative algorithms. These algorithms areoften computationally intensive and require robust estimatesof the same parameters to start the algorithms, but so far norobust starting values have been proposed for this model. Thispaper proposes computationally fast robust estimators forthe variance components under an unbalanced nested errormodel, based on a simple robustification of the fitting-ofconstantsmethod or Henderson method III. These estimatorscan be used as starting values for other iterative methods. Oursimulations show that they are highly robust to various typesof contamination of different magnitude

clustered data; linear mixed model; random effects; robust fitting; variance estimation

Fast and robust estimators of variance components in the nested error model Articles