Model Selection for Ordinary Differential Equations: A Statistical Testing Approach.

ABSTRACT: Ordinary differential equations (ODEs) are foundational tools in modeling intricate dynamics across a gamut of scientific disciplines. Yet, a possibility to represent a single phenomenon through multiple ODE models, driven by different understandings of nuances in internal mechanisms or abstraction levels, presents a model selection challenge. This study introduces a testing-based approach for ODE model selection amidst statistical noise. Rooted in the model misspecification framework, we adapt classical statistical paradigms (Vuong and Hotelling) to the ODE context, allowing for the comparison and ranking of diverse causal explanations without the constraints of nested models. Our simulation studies numerically investigate the statistical properties of the test, demonstrating its attainme

SUBMITTER: Dattner I 

PROVIDER: S-EPMC11604029 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature