mlsanityData to 14 Oct 2026

Bounded P-values in Parametric Programming-based Selective Inference

arXiv 2307.11351 ↗First posted 21 Jul 2023stat.ML cs.LGcited by 1

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Abstract (arXiv)

Selective inference (SI) has been actively studied as a promising framework for statistical hypothesis testing for data-driven hypotheses. The basic idea of SI is to make inferences conditional on an event that a hypothesis is selected. In order to perform SI, this event must be characterized in a traceable form. When selection event is too difficult to characterize, additional conditions are introduced for tractability. This additional conditions often causes the loss of power, and this issue is referred to as over-conditioning in [Fithian et al., 2014]. Parametric programming-based SI (PP-based SI) has been proposed as one way to address the over-conditioning issue. The main problem of PP-based SI is its high computational cost due to the need to exhaustively explore the data space. In this study, we introduce a procedure to reduce the computational cost while guaranteeing the desired precision, by proposing a method to compute the lower and upper bounds of p-values. We also proposed three types of search strategies that efficiently improve these bounds. We demonstrate the effectiveness of the proposed method in hypothesis testing problems for feature selection in linear models and attention region identification in deep neural networks.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 31 of its 59 references that are in the map. First posted on arXiv 21 Jul 2023.

Builds on

31 of 59 references.

References · 31

  1. 2013Exact post-selection inference, with application to the lassocited by 91
  2. 2019Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithmcited by 77
  3. 2014Optimal Inference After Model Selectioncited by 67
  4. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 48
  5. 2020Selective Inference for Hierarchical Clusteringcited by 39
  6. 2017Valid Inference Corrected for Outlier Removalcited by 26
  7. 2019Testing for a Change in Mean After Changepoint Detectioncited by 26
  8. 2020Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programmingcited by 25
  9. 2021More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingcited by 25
  10. 2019Computing Valid p-values for Image Segmentation by Selective Inferencecited by 24
  11. 2018More powerful post-selection inference, with application to the Lassocited by 23
  12. 2022Selective inference for k-means clusteringcited by 22
19 more
  1. 2016Selective Inference for Group-Sparse Linear Modelscited by 21
  2. 2020Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inferencecited by 21
  3. 2013A significance test for the lassocited by 20
  4. 2018Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Datacited by 19
  5. 2014Selecting the number of principal components: estimation of the true rank of a noisy matrixcited by 18
  6. 2014A significance test for forward stepwise model selectioncited by 17
  7. 2015Selective Sequential Model Selectioncited by 17
  8. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  9. 2021Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuationcited by 16
  10. 2021Fast and More Powerful Selective Inference for Sparse High-order Interaction Modelcited by 15
  11. 2021Tree-Values: selective inference for regression treescited by 15
  12. 2016Post Selection Inference with Kernelscited by 13
  13. 2017Selective Inference for Change Point Detection in Multi-dimensional Sequencescited by 11
  14. 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inferencecited by 10
  15. 2019Approximate selective inference via maximum likelihoodcited by 8
  16. 2021More powerful selective inference for the graph fused lassocited by 7
  17. 2021Exact Statistical Inference for the Wasserstein Distance by Selective Inferencecited by 7
  18. 2022Exact Selective Inference with Randomizationcited by 6
  19. 2017Selective inference for the problem of regions via multiscale bootstrapcited by 5

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023Selective Inference for Changepoint detection by Recurrent Neural Networkcited by 4