The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk

TL;DR

This paper introduces a new generalized ridge regression shrinkage path that is optimally short and passes through the best variance-bias trade-off estimators, enhancing interpretability and confidence in linear modeling.

Contribution

It proposes a novel, shortest possible ridge shrinkage path that passes through optimal variance-bias trade-off estimators, with new visualization tools for better data analysis.

Findings

Provides five types of ridge TRACE visualizations

Improves understanding of linear model fitting in ill-conditioned data

Enhances confidence in model selection and interpretation

Abstract

A new generalized ridge regression shrinkage path is proposed that is as short as possible under the restriction that it must pass through the vector of regression coefficient estimators that make the overall Optimal Variance-Bias Trade-Off under Normal distribution-theory. Five distinct types of ridge TRACE displays plus other graphics for this efficient path are motivated and illustrated here. These visualizations provide invaluable data-analytic insights and improved self-confidence to researchers and data scientists fitting linear models to ill-conditioned (confounded) data.