Improved LASSO

arXiv1503.05160MaRDI QIDQ6260052

A. K. Md. Ehsanes Saleh, Enayetur Raheem

Publication date: 17 March 2015

Abstract: 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 ($$), error variance ($sigm{a}^2$) 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 $Delt{a}^2$ as in the traditional case.

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