Model Robust Response Surface Designs: Scaling Two-Level Factorials
提出一个贝叶斯模型，假设响应函数近似不准确，基于该模型的设计准则为两水平因子设计选择合理的缩放比例，且缩放选择对先验分布不敏感。
Response surface methods use simple graduating functions to study the relationship between an experimental response variable and a set of continuous explanatory variables. In designing a response surface study, an experimenter must decide how far apart to set the levels of each factor, i.e. how to scale the design. A good choice should be sensitive to the fact that the graduating function is only an approximation to the true response function. A Bayesian model is proposed that makes explicit assumptions about inadequacy of an assumed model and a design criterion based on the model leads to reasonable choices of scale for two-level factorial designs. The choice of scale is found to insensitive to the prior distributions in the model.