Working Paper (2021)
Fernández-de-Marcos Alberto and García-Portugués Eduardo
By Zhang Jeff, Drovandi Christopher, Kypraios Theodore, and Papanikolaou Nikos
Working Paper (2017)
Functional response models are important in understanding predator-prey interactions. The development of functional response methodology has progressed from mechanistic models to more statistically motivated models that can account for variance and the over-dispersion commonly seen in the datasets collected from functional response experiments. However, little information seems to be available for those wishing to prepare optimal parameter estimation designs for functional response experiments. It is worth noting that optimally designed experiments may require smaller sample sizes to achieve the same statistical outcomes as non-optimally design experiments. In this paper, we develop a model-based approach to optimal experimental design for functional response experiments in the presence of parameter uncertainty (also known as a robust optimal design approach). Further, we develop and compare new utility functions which better focus on the statistical efficiency of the designs; these utilities are generally applicable for robust optimal design in other applications (not just in functional response). The methods are illustrated using a published beta-binomial functional response model for an experiment involving the freshwater predator \textit{Notonecta glauca} (an aquatic insect) preying on \textit{Asellus aquaticus} (a small crustacean) as a case study. As a by-product of the case study, we also derive necessary quantities to perform optimal design for beta-binomial regression models, which may be useful in other applications.
Zhang J., Drovandi C., Kypraios T., and Papanikolaou N. (2017) Optimal Experimental Design for Predator-Prey Functional Response Experiments. Working Paper.