Exact uniformly most powerful postselection confidence distributions
研究了在从一组可能设定错误的线性回归模型中选择一个后，如何构造条件置信分布，用于有效的后选择推断，这些分布是有限样本精确的，并包含所选模型中关注参数的所有信息。
Abstract A conditioning on the event of having selected one model from a set of possibly misspecified normal linear regression models leads to the construction of uniformly optimal conditional confidence distributions. They can be used for valid postselection inference. The constructed conditional confidence distributions are finite sample exact and encompass all information regarding the focus parameter in the selected model. This includes the construction of optimal postselection confidence intervals at all significance levels and uniformly most powerful hypothesis tests.