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Annual Review of Statistics and Its Application

Volume 9, 2022

Post-Selection Inference

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.

Keyword(s): data transformation, exploratory data analysis, model selection, post-selection inference, sample splitting, selective inference
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2022-03-07
2024-06-08
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/content/journals/10.1146/annurev-statistics-100421-044639
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Post-Selection Inference
Annual Review of Statistics and Its Application 9, 505 (2022); https://doi.org/10.1146/annurev-statistics-100421-044639
/content/journals/10.1146/annurev-statistics-100421-044639
/content/journals/10.1146/annurev-statistics-100421-044639
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