Possibilistic Inferential Models for Post-Selection Inference in High-Dimensional Linear Regression
高维线性回归中选择后推断的可能性推断模型
Valid uncertainty quantification after model selection remains challenging in high-dimensional linear regression, especially within the possibilistic inferential model (PIM) framework. We develop possibilistic inferential models for post-selection inference based on a regularized split possibilistic construction (RSPIM) that combines generic high-dimensional selectors with PIM validification through sample splitting. A first subsample is used to select a sparse model;…