T-Optimality for Model Discrimination in Non-Nested Models (medical studies)
Keywords:
T-optimality, Non-nested models, Model selection, Logistic regression, Probit regressionAbstract
T-optimality is a design criterion in model discrimination, aiming to maximize the power of a statistical test distinguishing between competing regression models. This article explores the use of T-optimality in the context of non-nested models, which do not share a common parameter space. We present the mathematical formulation, discuss its statistical properties, provide algorithmic considerations, and illustrate with numerical examples. Applications span medicine, engineering, and economics, where selecting the best predictive model is crucial.
Downloads
Published
Issue
Section
Categories
License
Copyright (c) 2025 Copyright © 2025 by the Author(s). Published by Neo-ART Excellence Hub Pvt. Ltd. This article is distributed under the Creative Commons Attribution 4.0 License (CC BY 4.0). Authors retain copyright and grant the publisher the right to publish and distribute the work. https://creativecommons.org/licenses/by/4.0/
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in the Journal of Clinical and Advanced Medicine (JCAM) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows anyone to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially, as long as appropriate credit is given to the original author(s) and the source.
License link: https://creativecommons.org/licenses/by/4.0/
