Abstract
In this paper, we concentrate on the generalized maximum entropy (GME) estimators. The aim is to improve the problem of multicollinearity in the linear mixed models (LMMs). Then the asymptotic properties of these estimators will be derived. Also, we obtain the Ridge-GME estimators, which combines ridge regression and GME, to enhance the problem of the traditional ridge regression and GME method in these models. Finally, a simulation study and a numerical example have been conducted to show the superiority of the Ridge-GME estimator over the ridge estimator (RE) and the maximum likelihood (ML) estimators.
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