Optimal Experimental Design for Another's Analysis
研究了当实验设计和数据分析由不同方进行时的最优实验设计问题，双方目标一致但先验信念可能不同，给出了在平方误差损失和对数效用下的样本量选择和分配结果。
Abstract We consider the optimal design of experiments in which estimation and design are performed by different parties. The parties are assumed to share similar goals, as reflected by a common loss function, but they may have different prior beliefs. After presenting a few motivating examples, we examine the problem of optimal sample size selection under a normal likelihood with constant cost per observation. We also consider the problem of optimal allocation for given overall sample sizes. We present results under both squared-error loss and a logarithmic utility, paying attention to the differences between one- and two-prior optimal designs. An asymmetric discrepancy measure features repeatedly in our development, and we question the extent of its role in optimal two-prior design.