报告题目：TUNE: Algorithm-Agnostic Inference after Changepoint Detection
报告人：王光辉（南开大学统计与数据科学学院副教授）
报告时间：2024年11月25日 10:30-11:30
报告地点：文波楼401会议室
摘要：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.
报告人简介: 王光辉，南开大学统计与数据科学学院副教授。2018年博士毕业于南开大学。2021-2024年曾任华东师范大学统计学院副教授。研究兴趣包括变点检测和高维数据推断等。在统计学和机器学习领域的权威期刊与会议上，如JRSSB、AoS、JMLR，以及NeurIPS和AAAI等，发表多篇论文。
