Empirical Bayes estimators of the random intercept in multilevel analysis: Performance of the classical, Morris and Rao version

Abstract

For a linear multilevel model with 2 levels, with equal numbers of level-1 units per level-2 unit and a random intercept only, different empirical Bayes estimators of the random intercept are examined. Studied are the classical empirical Bayes estimator, the Morris version of the empirical Bayes estimator and Rao's estimator. It is unclear which of these estimators performs best in terms of Bayes risk. Of these three, the Rao estimator is optimal in case the covariance matrix of random coefficients may be negative definite. However, in the multilevel model this matrix is restricted to be positive semi-definite. The Morris version, replaces the weights of the empirical Bayes estimator by unbiased estimates. This correction, however, is based on known level-1 variances, which in many empirical settings are unknown. A fourth estimator is proposed, a variant of Rao's estimator which restricts the estimated covariance matrix of random coefficients to be positive semi-definite. Since there are no closed-form expressions for estimators involved in the empirical Bayes estimators (except for the Rao estimator), Monte Carlo simulations are done to evaluate the performance of these different empirical Bayes estimators. Only for small sample sizes there are clear differences between these estimators. As a consequence, for larger sample sizes the formula for the Bayes risk of the Rao estimator can be used to calculate the Bayes risk for the other estimators proposed.