Estimation of linear models with missing data: The role of stochastic linear constraints

Assuming the nonavailability of some observations and the
availability of some stochastic linear constraints connecting the
coefficients in a linear regression, the technique of mixed
regression estimation is considered and a set of five unbiased
estimators for the vector of coefficeints is presented. They are
compared with respect to the criterion of variance covariance
matrix and conditions are obtained for the superiority of one
estimator over the other.