The TRACE display below shows how fitted regression coefficients for the infamous Longley(1967) dataset change due to shrinkage along a Q-shape = -1.5 path through 6-dimensional coefficient likelihood space.
Shrinkage methods can drastically reduce variability, but shrinkage also results in biased estimates. Since mean-squared-error risk is made up of variance plus squared-bias, shrinkage reduces risk whenever the unknown squared-bias introduced is less than the known reduction in (relative) variance.
To apply GRR Shrinkage methods, the two key questions that a regression practitioner must answer are:
Information on Various GRR Topics...
softRX freeware is proud to provide computer algorithms for R and several older systems to guide you along your GRR Shrinkage "journey" Our freeware provides powerful, Maximum Likelihood statistical inferences and dynamic Xlisp-Stat graphical insights along a "Path" of your choice!