Adaption of Akaike Information Criterion Under Least Squares Frameworks for Comparison of Stochastic Models
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.
Banks, H. T.; and Joyner, Michele L.. 2019. Adaption of Akaike Information Criterion Under Least Squares Frameworks for Comparison of Stochastic Models. Quarterly of Applied Mathematics. Vol.77(4). 831-859. https://doi.org/10.1090/qam/1542 ISSN: 0033-569X