Alternating Pruned Dynamic Programming for Multiple Epidemic Change-Point Estimation
Alternating Pruned Dynamic Programming for Multiple Epidemic Change-Point Estimation
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DOI：
10.1080/10618600.2020.1868304
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发表时间：
2019-07
影响因子：
2.4
通讯作者：
Zifeng Zhao;C. Yau
中科院分区：
文献类型：
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作者：
Zifeng Zhao;C. Yau
摘要本文研究了流行病背景下单变量序列的多个变点检测问题，其中序列的行为在一个共同的正常状态和不同的流行病状态之间交替。这是经典（单一）流行病变点测试问题的非平凡推广。为了明确地将交替结构的问题，我们提出了一种新的模型选择为基础的方法，同时推理的变点和交替状态。使用相同的精神轮廓似然，我们开发了一个两阶段交替修剪动态规划算法，进行高效和准确的优化模型选择标准，并作为最坏情况下的计算成本。大量数值实验表明，与经典的通用多变点检测方法相比，该方法提高了变点估计和模型参数估计的准确性。我们进一步展示了所提出的算法与本地聚类信号的多重测试有前途的应用，并证明了其优势，在大规模的多重测试，在DNA拷贝数变异检测，和海洋学研究的现有方法。这篇文章的补充材料可在网上查阅。
Abstract In this article, we study the problem of multiple change-point detection for a univariate sequence under the epidemic setting, where the behavior of the sequence alternates between a common normal state and different epidemic states. This is a nontrivial generalization of the classical (single) epidemic change-point testing problem. To explicitly incorporate the alternating structure of the problem, we propose a novel model selection based approach for simultaneous inference on both change-points and alternating states. Using the same spirit as profile likelihood, we develop a two-stage alternating pruned dynamic programming algorithm, which conducts efficient and exact optimization of the model selection criteria and has as the worst case computational cost. As demonstrated by extensive numerical experiments, compared to classical general-purpose multiple change-point detection procedures, the proposed method improves accuracy for both change-point estimation and model parameter estimation. We further show promising applications of the proposed algorithm to multiple testing with locally clustered signals, and demonstrate its advantages over existing methods in large scale multiple testing, in DNA copy number variation detection, and in oceanographic study. Supplementary material for this article is available online.