Summary: A generalized class of estimators is developed for the estimation of regression coefficients in the linear regression model under non-normal disturbances and incomplete prior information. The risk of the proposed estimator under a general quadratic loss function is derived following small-\(\sigma\) asymptotic approach. A comparative study is carried out and some better estimators in the sense of having smaller risk are found.