Estimation of Linear Regression Models with Missingness of Observations on Both the Explanatory and Study Variables-Part I: Theoretical Results

This paper discusses the estimation of coefficients in a linear
regression model when there are some missing observations on an
explanatory variable and the study variable individually as well as
simultaneously. The first order regression method of imputation is
followed and the least squares procedure is applied. Efficiency
properties of estimators are then investigated employing the large
sample asymptotic theory.