Estimation of Regression Coefficients Subject to Exact Linear Restrictions when some Observations are Missing and Balanced Loss Function is Used

This article considers a linear regression model when a set of
exact linear restrictions binding the coefficients is available and
some observations on the study variable are missing. Estimators for
the vectors of regression coefficients are presented and their
superiority properties with respect to the criteria of the variance
covariance matrix and the risk under balanced loss functions are
analyzed.