Post-Selection Inference

doi:10.1146/annurev-statistics-100421-044639

Post-Selection Inference

2022-03-07

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Arun Kumar Kuchibhotla *

DOI：10.1146/annurev-statistics-100421-044639

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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.

Keywords:

data transformation

exploratory data analysis

model selection

post-selection inference

sample splitting

selective inference

Post-Selection Inference

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