Preliminary test and Stein-type shrinkage LASSO-based estimators

Identifier:  RP:2470

Handle: https://hdl.handle.net/20.500.11797/RP2470

Authors:  Arashi, Mohammad; Norouzirad, Mina

Abstract:
Suppose the regression vector-parameter is subjected to lie in a subspace hypothesis in a linear regression model. In situations where the use of least absolute and shrinkage selection operator (LASSO) is desired, we propose a restricted LASSO estimator. To improve its performance, LASSO-type shrinkage estimators are also developed and their asymptotic performance is studied. For numerical analysis, we used relative efficiency and mean prediction error to compare the estimators which resulted in the shrinkage estimators to have better performance compared to the LASSO.