Adaption of Akaike information criterion under least squares frameworks for comparison of stochastic models
QUARTERLY OF APPLIED MATHEMATICS(2019)
Abstract
In this paper, we examine the feasibility of extending the Akaike information criterion (AIC) for deterministic systems as a potential model selection criteria for stochastic models. We discuss the implementation method for three different classes of stochastic models: continuous time Markov chains (CTMC), stochastic differential equations (SDE), and random differential equations (RDE). The effectiveness and limitations of implementing the AIC for comparison of stochastic models is demonstrated using simulated data from the three types of models and then applied to experimental longitudinal growth data for algae.
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Key words
Continuous time Markov chain models,CTMC,stochastic differential equations,SDE,random differential equations,RDE,inverse problems,model comparison techniques,Akaike information criterion,AIC
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