A common problem in multivariate general linear models is partially missing response data. The simplest method of analysis in the presencea of missing data has been to delete all observations on any individual with any missing data (listwise deletion) and utilize a traditional complete data approach. However, this can result in a great loss of information, and perhaps inconsistencies in the estimation of the variancecovariance matrix. In the generalized multivariate analysis of variance (GMANOVA) model with missing data, Kleinbaum (1973) proposed an estimated generalized least squares approach. In order to apply this, however, a consistent estimate of the variance-covariance matrix is needed. Kleinbaum proposed an estimator which is unbiased and consistent, but it does not take advantage of the fact that the underlying model is GMANOVA and not MANOVA. Using the fact that the underlying model is GMANOVA we have constructed four other consistent estimators. A Monte Carlo simulation experiment is conducted to further examine how well these estimators compare to the estimator proposed by Kleinbaum.
All Science Journal Classification (ASJC) codes
- Statistics and Probability