Exact selective inference with randomization
提出一种用于随机化下精确选择性推断的枢轴量，在高斯回归模型中可得到闭式解，并产生比数据拆分更窄的置信区间，在模拟和HIV耐药数据上验证了功效与精确推断的权衡。
Summary We introduce a pivot for exact selective inference with randomization. Not only does our pivot lead to exact inference in Gaussian regression models, but it is also available in closed form. We reduce this problem to inference for a bivariate truncated Gaussian variable. By doing so, we give up some power that is achieved with approximate maximum likelihood estimation in Panigrahi & Taylor (2023). Yet our pivot always produces narrower confidence intervals than a closely related data-splitting procedure. We investigate the trade-off between power and exact selective inference on simulated datasets and an HIV drug resistance dataset.