Post Hoc Inference for Component Attribution in Multivariate Change-Point Detection

arXiv:2607.14814 · stat.ME, math.ST, stat.ML · Submitted 2026-07-16 · Read on arXiv

Dhia-Elhaq Ouerfelli, Sylvain Arlot, Kevin Bleakley, Patrick Pamphile

stat.ME, math.ST, stat.ML

Submitted: 2026-07-16

Comments: 44 pages, 18 figures

Code: https://github.com/Dhia-Elhaq/Post-hoc-change-point-detection

License: http://creativecommons.org/licenses/by/4.0/

The gist: We consider the post-detection analysis of change-points for multivariate time series, with the goal of identifying which coordinates are responsible for a detected change.

Terminology

• Localization — 60× in this paper · explained in 2 episodes
• Theoretical Guarantees — 13× in this paper · explained in 2 episodes
• Accuracy — 12× in this paper · explained in 1 episode
• Selection Bias — 7× in this paper · explained in 5 episodes
• Kernel Change-Point — 6× in this paper
• Explainability — 4× in this paper · explained in 4 episodes
• Interpretability — 4× in this paper · explained in 15 episodes
• Maximum Mean Discrepancy — 4× in this paper
• Robustness — 4× in this paper · explained in 6 episodes
• Sparsity — 4× in this paper · explained in 5 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Hilbert Space — 3× in this paper · explained in 2 episodes
• Post-Selection — 3× in this paper · explained in 3 episodes
• Decoupling — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Grid-based Two-Sample Test — 2× in this paper
• Maximum Mean Discrepancy (MMD) — 2× in this paper · explained in 3 episodes
• Monotonicity — 2× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 4 episodes
• Calibration — 1× in this paper · explained in 9 episodes
• Clarity — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 12 episodes
• Distribution Shifts — 1× in this paper · explained in 2 episodes
• Inflation — 1× in this paper · explained in 2 episodes
• Initial Data — 1× in this paper · explained in 2 episodes
• Quantifying Uncertainty — 1× in this paper · explained in 5 episodes
• Sample Efficiency — 1× in this paper · explained in 7 episodes
• Signal-to-Noise Ratio (SNR) — 1× in this paper · explained in 6 episodes

Abstract

Sources

• Theoretical guarantees for change localization using conformal p-values
• Post-selection inference for quantifying uncertainty in changes in variance
• Online Centralized Non-parametric Change-point Detection via Graph-based Likelihood-ratio Estimation
• Large sample analysis of the median heuristic
• Variable Selection for Comparing High-dimensional Time-Series Data
• Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
• Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference
• Selective Inference for Change Point Detection in Multi-dimensional Sequences
• Variable Selection for Kernel Two-Sample Tests

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