We propose a general framework to construct self-normalized multiple-change-point tests with time series data. The only building block is a user-specified one-change-point detecting statistic, which covers a wide class of popular methods, including cumulative sum process, outlier-robust rank statistics and order statistics. Neither robust and consistent estimation of nuisance parameters, selection of bandwidth parameters, nor pre-specification of the number of change points is required. The finite-sample performance shows that our proposal is size-accurate, robust against misspecification of the alternative hypothesis, and more powerful than existing methods. Case studies of NASDAQ option volume and Shanghai-Hong Kong Stock Connect turnover are provided.

翻译：我们提出一个总框架,用时间序列数据构建自我标准化的多变点测试;唯一的基石是用户指定的一变点检测统计数据,该统计数据涵盖广泛的流行方法,包括累积总和过程、外部强压级统计和定购统计;不需要对骚扰参数、带宽参数的选择进行可靠和一致的估计,也不需要预先确定变更点的数目。 有限抽样表现表明,我们的提案规模准确,对替代假设的错误区分有力,比现有方法更强大。 提供了对NASDAQ选项量和上海-香港股票连接更替率的案例研究。