Use of prior information in the form of interval constraints for the improved estimation of linear regression models with some missing responses

We have considered the estimation of coefficients in a linear
regression model when some responses on the study variable are
missing and some prior information in the form of lower and upper
bounds for the average values of missing responses is available.
Employing the mixed regression framework, we have presented five
estimators for the vector of regression coefficients. Their exact
as well as asymptotic properties are discussed and superiority of
one estimator over the other is examined.