Generalized least squares estimation of multivariate nonlinear models with missing data

The method of estimated generalized least squares estimation of multiple response models is extended to the randomly missing date case. This estimation procedure is computationally simply when there are many missing data but the number of distinct patterns of missing data for the response vectors is small. The consistency and asymptotic normality of the proposed estimators are established.