We propose Robust Narrowest Significance Pursuit (RNSP), a methodology for detecting localised 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 is able to work 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.

翻译：我们提议用数据序列探测本地区域的方法,每个区域必须包含中位值的改变点,在规定的全球意义水平上进行。 RNSP采用新的标志-多分辨率光线型损失,在数据的许多区域安装假设的恒定模型,并贪婪地确定显著违反耐用性的最短间隔。RNSP在使用适合的模型候选人周围的数据标记时,可以在最低假设下工作,只要求真实残留物的标记对称和序列独立。特别是,它允许其异质性和任意重尾巴。RNSP返回的重大间隔具有一定的抽样性质,是无条件的,不依赖任何关于真实信号的假设。执行RNSP的代码可在https://github.com/pfryz/nspp上查阅。