Use of minimum risk approach in the estimation of regression models with missing observation

This article considers a linear regression model with some missing
observations on the response variable and presents two estimators
of regression coefficients employing the approach of minimum risk
estimation. Asymptotic properties of these estimators along with
the traditional unbiased estimator are analyzed and conditions,
that are easy to check in practice, for the superiority of one
estimator over the other are derived.