论文标题
最佳的预分析计划：统计决策可实施可行性
Optimal Pre-Analysis Plans: Statistical Decisions Subject to Implementability
论文作者
论文摘要
预先分析计划的目的是什么？应该如何设计它们？我们对分析数据的代理商与基于代理报告做出决定的委托人之间的相互作用进行建模。代理商可以是一种新药的制造商，而负责人则决定是否批准该药物。否则代理可以是提交研究论文的研究人员，而编辑决定是否发表研究论文。代理商决定要向本金报告哪些统计数据。本金无法验证分析师是否有选择地报告。如果没有利益冲突，则没有预先分析的消息，那么许多理想的决策规则将无法实施。允许代理在看到数据之前发送消息，增加了可以实施的决策规则集，并允许委托人利用代理专业知识。我们表征的最佳机制需要预先分析计划。将这些结果应用于假设检验，我们表明最佳拒绝规则预注册了有效的测试，并对未报告的统计数据做出了最坏的假设。最佳测试可以作为解决线性编程问题的解决方案。
What is the purpose of pre-analysis plans, and how should they be designed? We model the interaction between an agent who analyzes data and a principal who makes a decision based on agent reports. The agent could be the manufacturer of a new drug, and the principal a regulator deciding whether the drug is approved. Or the agent could be a researcher submitting a research paper, and the principal an editor deciding whether it is published. The agent decides which statistics to report to the principal. The principal cannot verify whether the analyst reported selectively. Absent a pre-analysis message, if there are conflicts of interest, then many desirable decision rules cannot be implemented. Allowing the agent to send a message before seeing the data increases the set of decision rules that can be implemented, and allows the principal to leverage agent expertise. The optimal mechanisms that we characterize require pre-analysis plans. Applying these results to hypothesis testing, we show that optimal rejection rules pre-register a valid test, and make worst-case assumptions about unreported statistics. Optimal tests can be found as a solution to a linear-programming problem.
