mlsanityData to 7 Oct 2026

Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data

arXiv 1812.03644 ↗First posted 10 Dec 2018stat.MEcited by 19

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Abstract (arXiv)

Changepoint detection methods are used in many areas of science and engineering, e.g., in the analysis of copy number variation data, to detect abnormalities in copy numbers along the genome. Despite the broad array of available tools, methodology for quantifying our uncertainty in the strength (or presence) of given changepoints, post-detection, are lacking. Post-selection inference offers a framework to fill this gap, but the most straightforward application of these methods results in low-powered tests and leaves open several important questions about practical usability. In this work, we carefully tailor post-selection inference methods towards changepoint detection, focusing as our main scientific application on copy number variation data. As for changepoint algorithms, we study binary segmentation, and two of its most popular variants, wild and circular, and the fused lasso. We implement some of the latest developments in post-selection inference theory: we use auxiliary randomization to improve power, which requires implementations of MCMC algorithms (importance sampling and hit-and-run sampling) to carry out our tests. We also provide recommendations for improving practical useability, detailed simulations, and an example analysis on array comparative genomic hybridization (CGH) data.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Dec 2018.

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Citing papers per year: 2021: 6; 2022: 3; 2023: 5; 2024: 1; 2025: 3; 2026: 1.

19 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. 2023Valid P-Value for Deep Learning-Driven Salient Regioncited by 17
  4. 2021Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuationcited by 16
  5. 2021Fast and More Powerful Selective Inference for Sparse High-order Interaction Modelcited by 15
  6. 2021Tree-Values: selective inference for regression treescited by 15
  7. 2021Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inferencecited by 10
  8. 2023CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inferencecited by 10
  9. 2021Exact Statistical Inference for the Wasserstein Distance by Selective Inferencecited by 7
  10. 2023Generalized Data Thinning Using Sufficient Statisticscited by 7
  11. 2023Selective Inference for Changepoint detection by Recurrent Neural Networkcited by 4
  12. 2024Controllable RANSAC-based Anomaly Detection via Hypothesis Testingcited by 2
7 more
  1. 2025Change Point Detection in the Frequency Domain with Statistical Reliabilitycited by 2
  2. 2025Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptationcited by 2
  3. 2023Bounded P-values in Parametric Programming-based 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. 2025Statistical Inference for Clustering-based Anomaly Detectioncited by 0
  7. 2026Testing hypotheses via orthogonalizationcited by 0