mlsanityData to 7 Oct 2026

Testing for a Change in Mean After Changepoint Detection

arXiv 1910.04291 ↗First posted 9 Oct 2019stat.MEcited by 25

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Abstract (arXiv)

While many methods are available to detect structural changes in a time series, few procedures are available to quantify the uncertainty of these estimates post-detection. In this work, we fill this gap by proposing a new framework to test the null hypothesis that there is no change in mean around an estimated changepoint. We further show that it is possible to efficiently carry out this framework in the case of changepoints estimated by binary segmentation and its variants, ℓ0 segmentation, or the fused lasso. Our setup allows us to condition on much less information than existing approaches, which yields higher powered tests. We apply our proposals in a simulation study and on a dataset of chromosomal guanine-cytosine content. These approaches are freely available in the R package ChangepointInference at https://jewellsean.github.io/changepoint-inference/.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 3; 2023: 6; 2024: 4; 2025: 4; 2026: 6.

25 papers.

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  1. 2021More Powerful Conditional Selective Inference for Generalized Lasso by Parametric Programmingcited by 24
  2. 2022Selective inference for k-means clusteringcited by 21
  3. 2021Tree-Values: selective inference for regression treescited by 15
  4. 2023CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inferencecited by 10
  5. 2023Generalized Data Thinning Using Sufficient Statisticscited by 7
  6. 2023Selective Inference for Changepoint detection by Recurrent Neural Networkcited by 4
  7. 2024Statistical Test for Feature Selection Pipelines by Selective Inferencecited by 3
  8. 2024Statistical Test for Auto Feature Engineering by Selective Inferencecited by 3
  9. 2025Quantifying Statistical Significance of Deep Nearest Neighbor Anomaly Detection via Selective Inferencecited by 3
  10. 2024Controllable RANSAC-based Anomaly Detection via Hypothesis Testingcited by 2
  11. 2025Change Point Detection in the Frequency Domain with Statistical Reliabilitycited by 2
  12. 2025Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptationcited by 2
13 more
  1. 2026Post Hoc Inference for Component Attribution in Multivariate Change-Point Detectioncited by 2
  2. 2023Bounded P-values in Parametric Programming-based Selective Inferencecited by 1
  3. 2024Statistical Test for Diffusion-Based Anomaly Localization via Selective Inferencecited by 1
  4. 2022Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detectioncited by 0
  5. 2022Inferring independent sets of Gaussian variables after thresholding correlationscited by 0
  6. 2023Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Seriescited by 0
  7. 2023Event Detection in Time Series: Universal Deep Learning Approachcited by 0
  8. 2025Statistical Inference for Clustering-based Anomaly Detectioncited by 0
  9. 2026Testing hypotheses via orthogonalizationcited by 0
  10. 2026ARM: Detector-Agnostic Changepoint Attribution with Finite-Sample Error Controlcited by 0
  11. 2026Automatic Statistical Test for Rationally Expressible Algorithms by Selective Inference, with Applications to Feature Selectioncited by 0
  12. 2026ARC: Augmented-Rank Conformalization for Changepoint Localization --- Finite-Sample Validity and Distribution-Robust Efficiencycited by 0
  13. 2026Post-Anomaly Detection Inference for Deep SVDDcited by 0