Maximum Likelihood Ridge Regression

07/25/2022
by   Robert L. Obenchain, et al.
0

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.

READ FULL TEXT
research
09/29/2022

Maximum likelihood estimation of the Weibull distribution with reduced bias

In this short note we derive a new bias-adjusted maximum likelihood esti...
research
11/07/2020

Maximum likelihood estimation for tensor normal models via castling transforms

In this paper, we study sample size thresholds for maximum likelihood es...
research
03/09/2021

The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk

A new generalized ridge regression shrinkage path is proposed that is as...
research
12/04/2017

Stochastic Maximum Likelihood Optimization via Hypernetworks

This work explores maximum likelihood optimization of neural networks th...
research
12/09/2019

Semiparametric Regression for Dual Population Mortality

Parameter shrinkage applied optimally can always reduce error and projec...
research
03/13/2023

Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review

The Targeted Maximum Likelihood Estimation (TMLE) statistical data analy...
research
05/06/2020

Maximum Likelihood Methods for Inverse Learning of Optimal Controllers

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

Please sign up or login with your details

Forgot password? Click here to reset