A design criterion for symmetric model discrimination based on nominal confidence sets

TL;DR

This paper introduces a symmetric model discrimination criterion using nominal confidence sets, enabling effective experimental design without prior knowledge of the true model, demonstrated through computational efficiency and enzyme kinetics application.

Contribution

It presents a novel symmetric criterion based on linearized distances and flexible nominal confidence sets, improving model discrimination without assuming the true model beforehand.

Findings

Computational efficiency demonstrated through simulations

Effective discrimination performance evaluated via Monte-Carlo methods

Successful application to enzyme kinetics models

Abstract

Experimental design applications for discriminating between models have been hampered by the assumption to know beforehand which model is the true one, which is counter to the very aim of the experiment. Previous approaches to alleviate this requirement were either symmetrizations of asymmetric techniques, or Bayesian, minimax and sequential approaches. Here we present a genuinely symmetric criterion based on a linearized distance between mean-value surfaces and the newly introduced tool of flexible nominal confidence sets. We demonstrate the computational efficiency of the approach using the proposed criterion and provide a Monte-Carlo evaluation of its discrimination performance on the basis of the likelihood ratio. An application for a pair of competing models in enzyme kinetics is given.