The analysis of record-breaking events is of interest in fields such as climatology, hydrology or anthropology. In connection with the record occurrence, we propose three distribution-free statistics for the changepoint detection problem. They are CUSUM-type statistics based on the upper and/or lower record indicators observed in a series. Using a version of the functional central limit theorem, we show that the CUSUM-type statistics are asymptotically Kolmogorov distributed. The main results under the null hypothesis are based on series of independent and identically distributed random variables, but a statistic to deal with series with seasonal component and serial correlation is also proposed. A Monte Carlo study of size, power and changepoint estimate has been performed. Finally, the methods are illustrated by analyzing the time series of temperatures at Madrid, Spain. The R package RecordTest publicly available on CRAN implements the proposed methods.

对破纪录事件的分析在气候学、水文学或人类学等领域很受关注。结合记录的出现，我们为变化点检测问题提出了三个无分布统计。它们是基于系列中观察到的上限和/或下限记录指标的 CUSUM 类型统计数据。使用功能中心极限定理的一个版本，我们表明 CUSUM 型统计量是渐近 Kolmogorov 分布的。原假设下的主要结果是基于一系列独立且同分布的随机变量，但也提出了处理具有季节性分量和序列相关性的序列的统计量。对大小、功率和变化点估计进行了蒙特卡罗研究。最后，通过分析西班牙马德里的温度时间序列来说明这些方法。R包CRAN 上公开可用的RecordTest实现了建议的方法。