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Bounded Influence Estimation in the Mixed Linear Model
Alice M. Richardson
Journal of the American Statistical Association
Vol. 92, No. 437 (Mar., 1997), pp. 154-161
Stable URL: http://www.jstor.org/stable/2291459
Page Count: 8
You can always find the topics here!Topics: Maximum likelihood estimation, Estimators, Statistical variance, Linear models, Statistical estimation, Outliers, Linear regression, Estimation methods, Estimation bias, Regression analysis
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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.
Journal of the American Statistical Association © 1997 American Statistical Association