标题： 构建局部自归一化多变点检验的一般框架 显示英文标题

标题： A General Framework For Constructing Locally Self-Normalized Multiple-Change-Point Tests

摘要： 我们提出了一种通用框架，用于构建具有时间序列数据的自归一化多变点检验。唯一的构建模块是一种用户指定的一变点检测统计量，涵盖了广泛流行的各类方法，包括累积和过程、鲁棒秩统计量和顺序统计量。该框架不需要对未知参数进行鲁棒且一致的估计、带宽参数的选择，也不需要预先指定变点的数量。有限样本性能显示，我们的提议具有准确的尺寸、对备择假设的误设具有鲁棒性，并且比现有方法更强大。提供了NASDAQ期权交易量和沪港通交易额的案例研究。 显示英文摘要

摘要： 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.