Abstract
We discuss inference after data exploration, with a particular focus on inference after model or variable selection. We review three popular approaches to this problem: sample splitting, simultaneous inference, and conditional selective inference. We explain how each approach works and highlight its advantages and disadvantages. We also provide an illustration of these post-selection inference approaches.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have