Inference after model selection

This work involves challenging mathematical and computational aspects of conducting inference after model selection procedures with complicated underlying geometry. This enables significance testing, for example, after using some of the most popular model selection procedures such as the lasso with regularization chosen by cross-validation, or forward stepwise with number of steps chosen by AIC or BIC. Often the resulting significance tests are slightly modified versions of the classical tests in regression analysis.

Joshua Loftus
2022-02-17

Post-selection inference

This work involves challenging mathematical and computational aspects of conducting inference after model selection procedures with complicated underlying geometry. This enables significance testing, for example, after using some of the most popular model selection procedures such as the lasso with regularization chosen by cross-validation, or forward stepwise with number of steps chosen by AIC or BIC. Often the resulting significance tests are slightly modified versions of the classical tests in regression analysis.

Software

I’m a co-author of the selectiveInference package on CRAN.

Publications

Reuse

Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".

Citation

For attribution, please cite this work as

Loftus (2022, Feb. 17). neurath's speedboat: Inference after model selection. Retrieved from http://joshualoftus.com/research/inference-after-model-selection/

BibTeX citation

@misc{loftus2022inference,
  author = {Loftus, Joshua},
  title = {neurath's speedboat: Inference after model selection},
  url = {http://joshualoftus.com/research/inference-after-model-selection/},
  year = {2022}
}