鲁棒最窄显著性追踪：中位数多重变点的推断
Robust Narrowest Significance Pursuit: Inference for multiple change-points in the median
AI总结：
本研究提出鲁棒最窄显著性追踪（RNSP）方法，通过符号多分辨率上确界范数损失和贪婪识别，在极少假设下检测数据序列中含中位数变点的局部区域，返回有限样本下的无条件显著性区间。
摘要：
我们提出鲁棒最窄显著性追踪（RNSP），这是一种用于检测数据序列中局部区域的方法，在指定的全局显著性水平下，每个局部区域都必然包含一个中位数变点。RNSP通过一种新的符号多分辨率上确界范数型损失，在数据的多个区域上拟合假设的常数模型，并贪婪地识别出常数性被显著违反的最短区间。通过围绕拟合的候选模型处理数据的符号，RNSP在极少假设下即可实现其覆盖性承诺，仅要求真实残差的符号具有符号对称性和序列独立性。具体而言，它允许残差存在异质性和任意重尾性。RNSP返回的显著性区间具有有限样本性质，本质上是无条件的，且不依赖于对真实信号的任何假设。实现RNSP的代码可在https://github.com/pfryz/nsp获取。
英文摘要：
We propose Robust Narrowest Significance Pursuit (RNSP), a methodology for detecting localized regions in data sequences which each must contain a change-point in the median, at a prescribed global significance level. RNSP works by fitting the postulated constant model over many regions of the data using a new sign-multiresolution sup-norm-type loss, and greedily identifying the shortest intervals on which the constancy is significantly violated. By working with the signs of the data around fitted model candidates, RNSP fulfils its coverage promises under minimal assumptions, requiring only sign-symmetry and serial independence of the signs of the true residuals. In particular, it permits their heterogeneity and arbitrarily heavy tails. The intervals of significance returned by RNSP have a finite-sample character, are unconditional in nature and do not rely on any assumptions on the true signal. Code implementing RNSP is available at https://github.com/pfryz/nsp.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
