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 shrinkage methodology, the __two key questions__
that a regression practitioner must answer are:

**softRX freeware** is proud to provide computer algorithms for
R, XLisp-Stat, Stata, Gauss and SAS-IMSL to guide you along your shrinkage
regression "journey" by providing powerful, __maximum likelihood__
statistical inferences and dynamic graphical insights along the
"route" of your choice! Related materials are...