Asymptotics of Selective Inference
Asymptotics of Selective Inference
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DOI：
10.1111/sjos.12261
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发表时间：
2015-01
影响因子：
1
通讯作者：
Xiaoying Tian;Jonathan E. Taylor
中科院分区：
文献类型：
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作者：
Xiaoying Tian;Jonathan E. Taylor
在本文中，我们试图建立渐近结果的选择性推理过程中删除高斯假设。我们考虑的选择过程的类是由仿射不等式，我们称之为仿射选择过程。仿射选择过程的示例包括沿着最小绝对收缩和选择算子（LASSO）的解路径的选择性推断，以及在正则化参数的固定值处拟合最小绝对收缩和选择算子之后的选择性推断。我们还考虑了惩罚广义线性模型中的一些检验。我们的结果证明了在高维环境下的渐近收敛性，其中n
In this paper, we seek to establish asymptotic results for selective inference procedures removing the assumption of Gaussianity. The class of selection procedures we consider are determined by affine inequalities, which we refer to as affine selection procedures. Examples of affine selection procedures include selective inference along the solution path of the least absolute shrinkage and selection operator (LASSO), as well as selective inference after fitting the least absolute shrinkage and selection operator at a fixed value of the regularization parameter. We also consider some tests in penalized generalized linear models. Our result proves asymptotic convergence in the high‐dimensional setting where n