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arXiv · 2409.15676

TUNE: Algorithm-Agnostic Inference after Changepoint Detection

Yinxu Jia·Jixuan Liu·Guanghui Wang·Zhaojun Wang·Changliang Zou
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Abstract

In multiple changepoint analysis, assessing the uncertainty of detected changepoints is crucial for enhancing detection reliability -- a topic that has garnered significant attention. Despite advancements through selective p-values, current methodologies often rely on stringent assumptions tied to specific changepoint models and detection algorithms, potentially compromising the accuracy of post-detection statistical inference. We introduce TUNE (Thresholding Universally and Nullifying change Effect), a novel algorithm-agnostic approach that uniformly controls error probabilities across detected changepoints. TUNE sets a universal threshold for multiple test statistics, applicable across a wide range of algorithms, and directly controls the family-wise error rate without the need for selective p-values. Through extensive theoretical and numerical analyses, TUNE demonstrates versatility, robustness, and competitive power, offering a viable and reliable alternative for model-agnostic post-detection inference.

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Yinxu Jia, Jixuan Liu, Guanghui Wang, Zhaojun Wang, Changliang Zou. 2024-09-24. TUNE: Algorithm-Agnostic Inference after Changepoint Detection. https://arxiv.org/abs/2409.15676

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