Multi-model mimicry for model selection according to generalised goodness-of-fit criteria

TL;DR

Multi-model mimicry (MMM) is a versatile model selection method that compares multiple models using generalized goodness-of-fit criteria, applicable across various modeling scenarios.

Contribution

This paper introduces a theoretical framework for MMM, broadening its applicability with generalized criteria and providing practical instructions for implementation.

Findings

MMM effectively compares non-nested models using generalized criteria

The method demonstrates broad applicability across different modeling contexts

Clear guidelines for applying MMM are provided

Abstract

Multi-model mimicry (MMM) is a flexible model selection technique for comparison of multiple, non-nested models on any desired goodness-of-fit criteria. Applicable to any set of candidate models that are 1) able to be fit to observed data, 2) can simulate new sets of data under the models, and 3) have a metric by which a dataset's goodness-of-fit to the model can be calculated, MMM has a much broader range of applicability than many standard model selection techniques. This manuscript highlights the previous literature whilst presenting the theoretical framework underpinning MMM. The scope of applicability is broadened through presentation of generalised criteria for comparison and the effectiveness of the method is demonstrated. Clear instruction for the application of MMM and the classification techniques required for model selection are also included.