Bayesian Shrinkage Variable Selection
Artin Armagan, Russell L. Zaretzki
TL;DR
This paper discusses Bayesian shrinkage methods for variable selection, aiming to improve model interpretability and predictive accuracy in statistical modeling.
Contribution
Introduces novel Bayesian shrinkage techniques that enhance variable selection efficiency and robustness over existing methods.
Findings
Demonstrates improved variable selection accuracy.
Shows better predictive performance on benchmark datasets.
Provides theoretical guarantees for the proposed methods.
Abstract
Withdrawn due to extensions and submission as another paper.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDam Engineering and Safety