基于有界变换的鲁棒变点检验
Robust change point tests by bounded transformations
摘要：
经典的基于矩的变点检验，如累积和检验，在具有一个变点的高斯时间序列的情况下非常强大，但在重尾分布和损坏的数据下表现不佳。提出了一类新的基于稳健变换观测值的累积和统计量的稳健变点检验方法。这个框架是相当灵活的，取决于所使用的变换，可以检测例如可能的多元时间序列的均值、尺度或依赖性的变化。仿真结果表明，这种方法是非常强大的，在检测的边缘方差的变化，重尾多元时间序列的依赖结构检测结构突变的过程中，优于现有的建议。
英文摘要：
Classical moment based change point tests like the cusum test are very powerful in case of Gaussian time series with one change point but behave poorly under heavy tailed distributions and corrupted data. A new class of robust change point tests based on cusum statistics of robustly transformed observations is proposed. This framework is quite flexible, depending on the used transformation one can detect for instance changes in the mean, scale or dependence of a possibly multivariate time series. Simulations indicate that this approach is very powerful in detecting changes in the marginal variance of ARCH processes and outperforms existing proposals for detecting structural breaks in the dependence structure of heavy tailed multivariate time series.
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。
