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Post-Selection Inference
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AI
DOI：10.1146/annurev-statistics-100421-044639.png)
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Abstract
En 中文
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
Journal
IF:
8.7
Papers:
211
Citations:
2.4K
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