mlsanityData to 7 Oct 2026

More powerful post-selection inference, with application to the Lasso

arXiv 1801.09037 ↗First posted 27 Jan 2018stat.MEcited by 22

Not on the map. Why?

Abstract (arXiv)

Investigators often use the data to generate interesting hypotheses and then perform inference for the generated hypotheses. P-values and confidence intervals must account for this explorative data analysis. A fruitful method for doing so is to condition any inferences on the components of the data used to generate the hypotheses, thus preventing information in those components from being used again. Some currently popular methods "over-condition", leading to wide intervals. We show how to perform the minimal conditioning in a computationally tractable way. In high dimensions, even this minimal conditioning can lead to intervals that are too wide to be useful, suggesting that up to now the cost of hypothesis generation has been underestimated. We show how to generate hypotheses in a strategic manner that sharply reduces the cost of data exploration and results in useful confidence intervals. Our discussion focuses on the problem of post-selection inference after fitting a lasso regression model, but we also outline its extension to a much more general setting.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Jan 2018.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 5; 2023: 4; 2024: 3; 2025: 4; 2026: 1.

22 papers.

Sort:

  1. 2021More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingcited by 24
  2. 2022Selective inference for k-means clusteringcited by 21
  3. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  4. 2021Fast and More Powerful Selective Inference for Sparse High-order Interaction Modelcited by 15
  5. 2021Tree-Values: selective inference for regression treescited by 15
  6. 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inferencecited by 10
  7. 2023CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inferencecited by 10
  8. 2021Exact Statistical Inference for the Wasserstein Distance by Selective Inferencecited by 7
  9. 2022Exact Selective Inference with Randomizationcited by 6
  10. 2024Statistical Test for Attention Map in Vision Transformercited by 6
  11. 2023Selective Inference for Changepoint detection by Recurrent Neural Networkcited by 4
  12. 2024Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptationcited by 2
10 more
  1. 2024Controllable RANSAC-based Anomaly Detection via Hypothesis Testingcited by 2
  2. 2025Change Point Detection in the Frequency Domain with Statistical Reliabilitycited by 2
  3. 2025Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptationcited by 2
  4. 2022Black-box Selective Inference via Bootstrappingcited by 1
  5. 2022Selective Inference for Sparse Multitask Regression with Applications in Neuroimagingcited by 1
  6. 2023Bounded P-values in Parametric Programming-based Selective Inferencecited by 1
  7. 2022Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detectioncited by 0
  8. 2025Post-Transfer Learning Statistical Inference in High-Dimensional Regressioncited by 0
  9. 2025Statistical Inference for Clustering-based Anomaly Detectioncited by 0
  10. 2026Finite-Sample Inference for Sparsely Permuted Linear Regressioncited by 0