SAS gives you the choice of two methods for estimating the parameters in the analysis: maximum likelihood (ML),
which we have encountered before, and restricted maximum likelihood (REML). The conventional wisdom seems
to be that ML produces more accurate estimates of fixed regression parameters, whereas REML produces more
accurate estimates of random variances (Twisk, 2006). As such, the choice of estimation procedure depends on
whether your hypotheses are focused on the fixed regression parameters or on estimating variances of the random
effects. However, in many situations the choice of ML or REML will make only a small difference to parameter
estimates. Also, if you want to compare models you must use ML.