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...**

## The MSE risk of Shrinkage...

## Influential Observations and Shrinkage in Xlisp-Stat

## Shrinkage Equations and MCAL

## GRR Reference List...

**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!