Maximum Likelihood Ridge Regression

by   Robert L. Obenchain, et al.

My first paper exclusively about ridge regression was published in Technometrics and chosen for invited presentation at the 1975 Joint Statistical Meetings in Atlanta. Unfortunately, that paper contained a wide range of assorted details and results. Luckily, Gary McDonald's published discussion of that paper focused primarily on my use of Maximum Likelihood estimation under normal distribution-theory. In this review of some results from all four of my ridge publications between 1975 and 2022, I highlight the Maximum Likelihood findings that appear to be most important in practical application of shrinkage in regression.


Maximum likelihood estimation of the Weibull distribution with reduced bias

In this short note we derive a new bias-adjusted maximum likelihood esti...

Maximum likelihood estimation for tensor normal models via castling transforms

In this paper, we study sample size thresholds for maximum likelihood es...

The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk

A new generalized ridge regression shrinkage path is proposed that is as...

Stochastic Maximum Likelihood Optimization via Hypernetworks

This work explores maximum likelihood optimization of neural networks th...

Semiparametric Regression for Dual Population Mortality

Parameter shrinkage applied optimally can always reduce error and projec...

Maximum Likelihood Methods for Inverse Learning of Optimal Controllers

This paper presents a framework for inverse learning of objective functi...

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