Fast estimation of multidimensional adaptive P-spline models
Mar\'ia Xos\'e Rodr\'iguez-\'Alvarez, Mar\'ia Durb\'an, Dae-Jin, Lee, Paul H. C. Eilers
TL;DR
This paper introduces a fast and stable algorithm called SOP for estimating multidimensional adaptive P-spline models, extending the SAP algorithm to improve efficiency and stability in smoothing parameter estimation.
Contribution
The paper presents the SOP algorithm, an extension of SAP, specifically designed for efficient and stable estimation of multidimensional adaptive P-spline models.
Findings
The SOP algorithm significantly improves computational speed.
The method enhances stability in multidimensional P-spline estimation.
It effectively extends SAP to adaptive multidimensional models.
Abstract
A fast and stable algorithm for estimating multidimensional adaptive P-spline models is presented. We call it as Separation of Overlapping Penalties (SOP) as it is an extension of the \textit{Separation of Anisotropic Penalties} (SAP) algorithm. SAP was originally derived for the estimation of the smoothing parameters of a multidimensional tensor product P-spline model with anisotropic penalties.
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
TopicsImage and Signal Denoising Methods · Statistical Methods and Inference · Statistical and numerical algorithms