Weighted Modified First Order Regression Procedures for Estimation in Linear Models with Missing X-Observations

This paper considers the estimation of coefficients in a linear
regression model with missing observations in the independent
variables and introduces a modification of the standard first order
regression method for imputation of missing values. The
modification provides stochastic values for imputation and, as an
extension, makes use of the principle of weighted mixed regression.
The proposed procedures are compared with two popular
procedures-one which utilizes only the complete observations and
the other which employs the standard first order regression
imputation method for missing values. A simulation experiment to
evaluate the gain in efficiency and to examine interesting issues
like the impact of varying degree of multicollinearity in
explanatory variables is proceeded. Some work on the case of
discrete regressor variables is in progress and will be reported in
a future article to follow.