Bounded Influence Estimation in the Mixed Linear Model

Abstract Bounded influence estimation (also known as generalized M or GM estimation) in the regression model is reviewed. The definitions of bounded influence estimation proposed by Mallows and Schweppe are then extended to the mixed linear model. This is achieved by applying appropriate weight functions to maximum likelihood and restricted maximum likelihood estimating equations. The asymptotic properties of the new estimators are obtained, and the estimators are applied to an artificial dataset. The article concludes with an extension of the example into a small simulation study designed to test some properties of the estimators in samples of moderate size.

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