An Optimal Experimental Design Criterion for Discriminating Between Non-Normal Models
区分非正态模型的最优实验设计准则
Summary Typically T-optimality is used to obtain optimal designs to discriminate between homoscedastic models with normally distributed observations. Some extensions of this criterion have been made for the heteroscedastic case and binary response models in the literature. In this paper, a new criterion based on the Kullback–Leibler distance is proposed to discriminate between rival models with non-normally distributed observations. The criterion is coherent with the…