A Predictive Approach to the Analysis of Designed Experiments
将设计实验的分析视为模型选择问题，引入基于复制实验预测密度的贝叶斯准则，并与其他常用准则比较，通过实例说明该方法。
Abstract Viewing the analysis of designed experiments as a model selection problem, we introduce the use of a predictive Bayesian criterion in this context based on the predictive density of a replicate experiment (PDRE). A calibration of the criterion is provided to assist in the model choice. The relationships of the proposed criterion to other prevalent criteria, such as AIC, BIC, and Mallows's C p , are given. An information theoretic criterion based on the PDRE's of two competing models is also introduced and compared with the usual F statistic for two nested models. Examples are given to illustrate the proposed methodology.