The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk

03/09/2021
by   Robert L. Obenchain, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/28/2020

Ridge TRACE Diagnostics

We describe a new p-parameter generalized ridge-regression shrinkage-pat...
research
07/25/2022

Maximum Likelihood Ridge Regression

My first paper exclusively about ridge regression was published in Techn...
research
05/14/2023

Nonparametric Generalized Ridge Regression

A Two-Stage approach enables researchers to make optimal non-linear pred...
research
11/30/2020

Tuning in ridge logistic regression to solve separation

Separation in logistic regression is a common problem causing failure of...
research
06/12/2023

Nonlinear Generalized Ridge Regression

A Two-Stage approach is described that literally "straighten outs" any p...
research
10/15/2021

Multiple Observers Ranked Set Samples for Shrinkage Estimators

Ranked set sampling (RSS) is used as a powerful data collection techniqu...
research
09/11/2023

Liu-type Shrinkage Estimators for Mixture of Poisson Regressions with Experts: A Heart Disease Study

Count data play a critical role in medical research, such as heart disea...

Please sign up or login with your details

Forgot password? Click here to reset