# The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk

arXiv: https://arxiv.org/abs/2103.05161 · HTML: https://mlsanity.com/paper/2103.05161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 9 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [Ridge Regularization: an Essential Concept in Data Science](https://mlsanity.com/paper/2006.00371/) (2020)
- [Ridge TRACE Diagnostics](https://mlsanity.com/paper/2005.14291/) (2020)

## Abstract (arXiv)

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.