Nonlinear Generalized Ridge Regression
Methodology
2023-10-03 v5
Abstract
A Two-Stage approach is described that literally "straighten outs" any potentially nonlinear relationship between a y-outcome variable and each of p = 2 or more potential x-predictor variables. The y-outcome is then predicted from all p of these "linearized" spline-predictors using the form of Generalized Ridge Regression that is most likely to yield minimal MSE risk under Normal distribution-theory. These estimates are then compared and contrasted with those from the Generalized Additive Model that uses the same x-variables.
Cite
@article{arxiv.2306.07396,
title = {Nonlinear Generalized Ridge Regression},
author = {Robert L. Obenchain},
journal= {arXiv preprint arXiv:2306.07396},
year = {2023}
}
Comments
10 pages, 4 Figures, 3 Tables, 11 References