• Corpus ID: 231786296

Splitting strategies for post-selection inference

@inproceedings{Rasines2021SplittingSF,
  title={Splitting strategies for post-selection inference},
  author={Daniel Garcia Rasines and G. Alastair Young},
  year={2021}
}
We consider the problem of providing valid inference for a selected parameter in a sparse regression setting. It is well known that classical regression tools can be unreliable in this context due to the bias generated in the selection step. Many approaches have been proposed in recent years to ensure inferential validity. Here, we consider a simple alternative to data splitting based on randomising the response vector, which allows for higher selection and inferential power than the former and… 

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