Nonparametric Generalized Ridge Regression
Abstract
A Two-Stage approach enables researchers to make optimal non-linear predictions via Generalized Ridge Regression using models that contain two or more x-predictor variables and make only realistic minimal assumptions. The optimal regression coefficient estimates that result are either unbiased or most likely to have mininal MSE risk under Normal distribution theory. All necessary calculations and graphical displays are generated using current versions of CRAN R-packages. A numerical example using the "corrected" USArrests data.frame introduces and illustrates this new robust statistical methodology. While applying this strategy to regression models with several hundred observations is straight-forward, the computations required in such cases can be extensive.
Cite
@article{arxiv.2305.08235,
title = {Nonparametric Generalized Ridge Regression},
author = {Robert L. Obenchain},
journal= {arXiv preprint arXiv:2305.08235},
year = {2023}
}
Comments
The title of this paper is potentially misleading, its methods are neither innovative nor easy to apply, and its numerical example is nearly trivial