  We propose an improved LASSO estimation technique based on Stein-rule. We
shrink classical LASSO estimator using preliminary test, shrinkage, and
positive-rule shrinkage principle. Simulation results have been carried out for
various configurations of correlation coefficients ($r$), size of the parameter
vector ($\beta$), error variance ($\sigma^2$) and number of non-zero
coefficients ($k$) in the model parameter vector. Several real data examples
have been used to demonstrate the practical usefulness of the proposed
estimators. Our study shows that the risk ordering given by LSE $>$ LASSO $>$
Stein-type LASSO $>$ Stein-type positive rule LASSO, remains the same uniformly
in the divergence parameter $\Delta^2$ as in the traditional case.
