论文标题
随机化的精确选择性推断
Exact Selective Inference with Randomization
论文作者
论文摘要
我们引入了一个枢轴，以进行随机化的精确选择性推断。我们的枢轴不仅会导致高斯回归模型的精确推断，而且还以封闭形式获得。我们将精确选择性推断的问题减少到双变量截短的高斯分布。通过这样做，我们放弃了在Panigrahi和Taylor（2022）中进行近似最大似然估计来实现的能力。然而，与紧密相关的数据拆分程序相比，我们的枢轴总是产生较窄的置信区间。我们研究了模拟数据集对功率和精确选择性推断与艾滋病毒耐药性数据集之间的权衡。
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 the problem of exact selective inference to a bivariate truncated Gaussian distribution. By doing so, we give up some power that is achieved with approximate maximum likelihood estimation in Panigrahi and Taylor (2022). 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.
