发表机构
Shanghai Jiao Tong University; Fudan University; National University of Singapore（上海交通大学; 复旦大学; 新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出 POIS 和 POIS+ 两种无分布假设的区间定位方法，通过保序样本拆分和检测条件 e 值控制区间级 FDR，实现多变点检测的误差率控制与高功效。
AI 中文摘要
多变点检测中的区间定位旨在识别包含真实变点的区间，同时控制错误发现。我们将此任务表述为对检测器报告区间进行的多重检验问题，并开发了一种与算法无关且无分布假设的框架，该框架控制区间层面的错误发现率（FDR），即保留区间中不含真实变点的期望比例。所提出的基于置换的保序区间选择（POIS）方法利用保序样本拆分将检测与置换检验分离。为提高功效，POIS+ 重用检测侧证据，并引入检测条件 e 值作为置换 p 值的非归一化权重。两种方法均实现了有限样本下的 FDR 控制。在所述条件下，POIS 达到渐近完全功效，而 POIS+ 的渐近功效不低于 POIS。模拟结果表明，POIS 保持高功效，POIS+ 进一步提高了功效，且两种方法的经验 FDR 均低于名义水平。两个真实数据应用证明了所提出方法的实际有效性。
英文摘要
Interval localization in multiple change-point detection aims to identify intervals containing true change-points while controlling false discoveries. We formulate this task as a multiple testing problem over detector-reported intervals and develop an algorithm-agnostic and distribution-free framework that controls the interval-level FDR, the expected proportion of retained intervals containing no true change-points. The proposed permutation-based order-preserving interval selection (POIS) uses order-preserving sample splitting to separate detection from permutation testing. To improve power, POIS+ reuses the detection-side evidence and introduces detection-conditional $e$-values as unnormalized weights for permutation $p$-values. Both procedures achieve finite-sample FDR control. Under the stated conditions, POIS attains asymptotically full power while POIS+ has asymptotically no lower power than POIS. Simulation results show that POIS maintains high power and POIS+ further improves power with empirical FDR below the nominal level for both procedures. Two real data applications demonstrate the practical effectiveness of the proposed methods.
