Cabrieto Jedelyn, Tuerlinckx Francis, Kuppens Peter, Grassmann Mariel, Ceulemans Eva
Quantitative Psychology and Individual Differences Research Group, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, B-3000, Belgium.
Health Psychology Research Group, KU Leuven - University of Leuven, Tiensestraat 102, Leuven, B-3000, Belgium.
Behav Res Methods. 2017 Jun;49(3):988-1005. doi: 10.3758/s13428-016-0754-9.
Change point detection in multivariate time series is a complex task since next to the mean, the correlation structure of the monitored variables may also alter when change occurs. DeCon was recently developed to detect such changes in mean and\or correlation by combining a moving windows approach and robust PCA. However, in the literature, several other methods have been proposed that employ other non-parametric tools: E-divisive, Multirank, and KCP. Since these methods use different statistical approaches, two issues need to be tackled. First, applied researchers may find it hard to appraise the differences between the methods. Second, a direct comparison of the relative performance of all these methods for capturing change points signaling correlation changes is still lacking. Therefore, we present the basic principles behind DeCon, E-divisive, Multirank, and KCP and the corresponding algorithms, to make them more accessible to readers. We further compared their performance through extensive simulations using the settings of Bulteel et al. (Biological Psychology, 98 (1), 29-42, 2014) implying changes in mean and in correlation structure and those of Matteson and James (Journal of the American Statistical Association, 109 (505), 334-345, 2014) implying different numbers of (noise) variables. KCP emerged as the best method in almost all settings. However, in case of more than two noise variables, only DeCon performed adequately in detecting correlation changes.
多元时间序列中的变点检测是一项复杂的任务，因为当发生变化时，除了均值之外，被监测变量的相关结构也可能改变。DeCon是最近开发的一种方法，通过结合移动窗口方法和稳健主成分分析来检测均值和/或相关性的此类变化。然而，在文献中，还提出了其他几种使用其他非参数工具的方法：E-划分法、多秩法和KCP法。由于这些方法使用不同的统计方法，因此需要解决两个问题。首先，应用研究人员可能会发现很难评估这些方法之间的差异。其次，仍然缺乏对所有这些方法在捕获表明相关性变化的变点方面的相对性能的直接比较。因此，我们介绍了DeCon、E-划分法、多秩法和KCP法背后的基本原理以及相应的算法，以使读者更容易理解。我们还通过广泛的模拟比较了它们的性能，这些模拟使用了Bulteel等人（《生物心理学》，98(1)，29 - 42，2014）的设置，意味着均值和相关结构的变化，以及Matteson和James（《美国统计协会杂志》，109(505)，334 - 345，2014）的设置，意味着不同数量的（噪声）变量。在几乎所有设置中，KCP法都是最佳方法。然而，在存在两个以上噪声变量的情况下，只有DeCon法在检测相关性变化方面表现良好。
