Continuous model averaging for benchmark dose analysis: Averaging over distributional forms
When estimating a benchmark dose (BMD) from chemical toxicity experiments, model averaging is recommended by the World Health Organization and European Food Safety Authority. Though model averaging iswell studied forBMD estimation using dichotomous data, few studies investigate it forBMDestimation using continuous data. In this setting, model averaging a BMD pose additional problems as the assumed distribution is essential to many BMD de?nitions, and distributional uncertainty is underestimated when one distribution is chosen a priori. As model averaging averages over a probability model, there is no reason one cannot include distributional assumptions into the model average. Consequently, we de?ne a continuous model averaging approach over distributional models and show that it is superior to single distribution model averaging.