A Geometric Approach to Optimal Design for One-Parameter Non-Linear Models
提出一种几何框架，用于构造单参数非线性模型在两点先验分布下的最优贝叶斯设计和极大极小设计，并通过逻辑回归和指数模型示例展示其应用。
SUMMARY A geometric framework for constructing optimal Bayesian designs and maximin designs for non-linear models with a single unknown parameter and a prior distribution on that parameter, which is restricted in that it comprises exactly two points of support, is presented. The approach is illustrated by means of selected examples involving logistic regression and the simple exponential model, and its applicability to the construction of optimal designs for models with uncontrolled variation and to model robust designs is also demonstrated. In addition, the method is shown to provide some valuable insights into the general properties of optimal Bayesian designs for non-linear models.