Nonparametric data segmentation in multivariate time series via joint characteristic functions
提出一种多变量时间序列的非参数分割方法，通过联合特征函数检测边际分布和非线性序列依赖中的变点，无需预设变化类型，理论证明一致性，在地震学和经济学数据中表现良好。
Summary Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial dependence, all without the need to prespecify the type of changes. We show the theoretical consistency of our method in estimating the total number and the locations of the changepoints, and demonstrate its good performance against a variety of changepoint scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.