Date of Award


Degree Type


Degree Name

Master of Science (MS)


Mathematics and Statistics

Committee Chair(s)

David White


David White


Missing data can often cause many problems in research work. Therefore for carrying out analysis, some procedure for obtaining estimates in the presence of missing data should be applied. Various theories and techniques have been developed for different types of problems.

Analysis of the Multivariate Normal Distribution with missing data is one of the areas studied. It has been discussed earlier by Wilkes (1932), Lord (1955), Edgett (1956) and Hartley (1958). They have established some basic concepts and an outline in the way of estimation.

In the last ten years, A. A. Afifi and R. M. Elasfoff also have contributed some important techniques in estimating the parameters respective to mean, variance and covariance. R.R. Hocking, H. H. Oxpring and W. B. Smith are continuously improving it toward a more practical method of calculation. In their paper (1971), they gave the derivation of equations and a numerical example without explanation of the details.

The main purpose of this report is to evaluate the reliability and feasibility of this method by programming it. The procedure will be a general one avaliable for large samples so that research workers can apply it conveniently in estimating the parameters when some observations are missing.