  This paper considers a multiple regression model and compares, under full
model hypothesis, analytically as well as by simulation, the performance
characteristics of some popular penalty estimators such as ridge regression,
LASSO, adaptive LASSO, SCAD, and elastic net versus Least Squares Estimator,
restricted estimator, preliminary test estimator, and Stein-type estimators
when the dimension of the parameter space is smaller than the sample space
dimension. We find that RR uniformly dominates LSE, RE, PTE, SE and PRSE while
LASSO, aLASSO, SCAD, and EN uniformly dominates LSE only. Further, it is
observed that neither penalty estimators nor Stein-type estimator dominate one
another.
