THE ESTIMATION OF FIXED EFFECTS IN A MIXED LINEAR MODEL

Abstract

The estimation of fixed effects is considered for small, unbalanced, mixed linear models. The two-stage estimator, in which the variance components are first estimated by ML or REML, is compared to the intra-block (IB) estimator, the ordinary least squares (OLS) estimator (ignoring the random effects) and the Gauss-Markov (GM) estimator. Comparison is made, based on 100 simulated data sets each, for 6 designs (3 BIBD's and 3 unbalanced designs). In comparing loss of information, relative to the GM lower bound, the two-stage procedures (using either ML or REML) are recommended for all but the smallest and least balanced design. The study also compared estimates of the variance of the two-stage estimators, using either the GM lower bound or the Kackar-Harville (KH) approximation. Estimators of the variance using REML estimates of the variance components are recommended, since estimators using ML estimates were seriously biased downward for all designs considered.