mlsanityData to 14 Oct 2026

Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference

arXiv 2110.08989 ↗First posted 18 Oct 2021stat.ML cs.LGcited by 10

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Abstract (arXiv)

In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence. In CP detection from a multi-dimensional sequence, it is often desirable not only to detect the location, but also to identify the subset of the components in which the change occurs. Several algorithms have been proposed for such problems, but no valid exact inference method has been established to evaluate the statistical reliability of the detected locations and components. In this study, we propose a method that can guarantee the statistical reliability of both the location and the components of the detected changes. We demonstrate the effectiveness of the proposed method by applying it to the problems of genomic abnormality identification and human behavior analysis.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 19 of its 60 references that are in the map. First posted on arXiv 18 Oct 2021.

Builds on

19 of 60 references.

References · 19

  1. 2016Deep, Convolutional, and Recurrent Models for Human Activity Recognition using Wearablescited by 94
  2. 2013Exact post-selection inference, with application to the lassocited by 91
  3. 2014Optimal Inference After Model Selectioncited by 67
  4. 2014Exact Post-Selection Inference for Sequential Regression Procedurescited by 48
  5. 2017Valid Inference Corrected for Outlier Removalcited by 26
  6. 2020Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programmingcited by 25
  7. 2016Multiple-change-point detection for high dimensional time series via sparsified binary segmentationcited by 24
  8. 2019Computing Valid p-values for Image Segmentation by Selective Inferencecited by 24
  9. 2018More powerful post-selection inference, with application to the Lassocited by 23
  10. 2018Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Datacited by 19
  11. 2014A significance test for forward stepwise model selectioncited by 17
  12. 2021Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuationcited by 16
7 more
  1. 2016Post Selection Inference with Kernelscited by 13
  2. 2017Selective Inference for Change Point Detection in Multi-dimensional Sequencescited by 11
  3. 2018Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimatorcited by 11
  4. 2015Selective inference in regression models with groups of variablescited by 9
  5. 2018Selective review of offline change point detection methodscited by 8
  6. 2021Exact Statistical Inference for the Wasserstein Distance by Selective Inferencecited by 7
  7. 2016Change-point detection in panel data via double CUSUM statisticcited by 5

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Cited by year (2026 to 14 Oct)

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

10 papers.

  1. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  2. 2023CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inferencecited by 10
  3. 2023Selective Inference for Changepoint detection by Recurrent Neural Networkcited by 4
  4. 2024Controllable RANSAC-based Anomaly Detection via Hypothesis Testingcited by 2
  5. 2025Change Point Detection in the Frequency Domain with Statistical Reliabilitycited by 2
  6. 2025Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptationcited by 2
  7. 2026Post Hoc Inference for Component Attribution in Multivariate Change-Point Detectioncited by 2
  8. 2023Bounded P-values in Parametric Programming-based Selective Inferencecited by 1
  9. 2022Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detectioncited by 0
  10. 2025Statistical Inference for Clustering-based Anomaly Detectioncited by 0