all AI news
The Efficient Shrinkage Path: Maximum Likelihood of Minimum MSE Risk
Feb. 19, 2024, 5:44 a.m. | Robert L. Obenchain
stat.ML updates on arXiv.org arxiv.org
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 …
abstract arxiv bias distribution five generalized likelihood normal path regression ridge risk shrinkage stat.co stat.me stat.ml theory through trade trade-off type types variance vector
More from arxiv.org / stat.ML updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
AI Engineering Manager
@ M47 Labs | Barcelona, Catalunya [Cataluña], Spain