论文标题
串行依赖性下的多个变化点检测：野生对比度最大化和Gappy Schwarz算法
Multiple change point detection under serial dependence: Wild contrast maximisation and gappy Schwarz algorithm
论文作者
论文摘要
我们提出了一种方法，用于检测原本固定的，自相关的线性时间序列的平均值。它结合了基于野生对比最大化原理的解决方案路径的生成，并将基于信息标准的模型选择策略称为Gappy Schwarz算法。前者非常适合将平均值与由于串行相关引起的波动分离，而后者同时估算了依赖性结构和变化点的数量，而无需执行估计噪声水平的艰难任务，例如长期差异。我们提供了对其理论特性的模块化研究，并表明称为WCM.GSA的组合方法在估计变更点的总数和位置方面达到了一致性。 WCM.GSA的良好性能通过广泛的仿真研究来证明，我们通过将方法应用于伦敦的空气质量数据，进一步说明了其有用性。
We propose a methodology for detecting multiple change points in the mean of an otherwise stationary, autocorrelated, linear time series. It combines solution path generation based on the wild contrast maximisation principle, and an information criterion-based model selection strategy termed gappy Schwarz algorithm. The former is well-suited to separating shifts in the mean from fluctuations due to serial correlations, while the latter simultaneously estimates the dependence structure and the number of change points without performing the difficult task of estimating the level of the noise as quantified e.g.\ by the long-run variance. We provide modular investigation into their theoretical properties and show that the combined methodology, named WCM.gSa, achieves consistency in estimating both the total number and the locations of the change points. The good performance of WCM.gSa is demonstrated via extensive simulation studies, and we further illustrate its usefulness by applying the methodology to London air quality data.
