Change-point detection for multivariate and non-Euclidean data with local dependency
Change-point detection for multivariate and non-Euclidean data with local dependency
复制标题
具有局部依赖性的多元和非欧几里得数据的变点检测
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Hao Chen
中科院分区：
文献类型：
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作者：
Hao Chen
在一系列多元观测值或非欧几里得数据对象（例如网络）中，局部依赖性很常见，并且可能导致错误的更改点发现。我们提出了一种新的排列方式 - 带有随机起点的圆形块排列 - 以解决此问题。基于观测值构建的相似性图，研究了该置换方案在非参数变更点检测框架上进行了研究，从而导致了具有局部依赖性数据的数据检测的一般框架。仿真研究表明，当没有局部依赖性时，该新框架将保留相同的功率，而它可以正确控制I型错误，以正确地控制具有和没有局部依赖性的序列。我们还在此新框架下得出了一个分析P值近似。该近似值适用于数百个及以上长度的序列，这使得该方法可快速应用，用于长数据序列。
In a sequence of multivariate observations or non-Euclidean data objects, such as networks, local dependence is common and could lead to false change-point discoveries. We propose a new way of permutation -- circular block permutation with a random starting point -- to address this problem. This permutation scheme is studied on a non-parametric change-point detection framework based on a similarity graph constructed on the observations, leading to a general framework for change-point detection for data with local dependency. Simulation studies show that this new framework retains the same level of power when there is no local dependency, while it controls type I error correctly for sequences with and without local dependency. We also derive an analytic p-value approximation under this new framework. The approximation works well for sequences with length in hundreds and above, making this approach fast-applicable for long data sequences.
影响因子：
2.1
作者：
Olshen, AB;Venkatraman, ES;Wigler, M
通讯作者：
Wigler, M