Sequential (Quickest) Change Detection: Classical Results and New Directions

Xie, Liyan; Zou, Shaofeng; Xie, Yao; Veeravalli, Venugopal V.

doi:10.1109/jsait.2021.3072962

Cited by 85 publications

(50 citation statements)

References 196 publications

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“…observations, and recently there has also been much development in CUSUM for non i.i.d. observations (Tartakovsky et al, 2014;Xie et al, 2021).…”

Section: Cusum Detection Proceduresmentioning

confidence: 94%

Sequential change-point detection for mutually exciting point processes over networks

Wang¹,

Xie²,

Xie

et al. 2021

Preprint

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We present a new CUSUM procedure for sequentially detecting change-point in the self and mutual exciting processes, a.k.a. Hawkes networks using discrete events data.Hawkes networks have become a popular model for statistics and machine learning due to their capability in modeling irregularly observed data where the timing between events carries a lot of information. The problem of detecting abrupt changes in Hawkes networks arises from various applications, including neuronal imaging, sensor network and social network monitoring. Despite this, there has not been a computationally and memory-efficient online algorithm for detecting such changes from sequential data.We present an efficient online recursive implementation of the CUSUM statistic for Hawkes processes, both decentralized and memory-efficient, and establish the theoretical properties of this new CUSUM procedure. We then show that the proposed CUSUM method achieves better performance than existing methods, including the Shewhart procedure based on count data, the generalized likelihood ratio (GLR) in the existing literature, and the standard score statistic. We demonstrate this via a simulated example and an application to population code change-detection in neuronal networks.

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“…observations, and recently there has also been much development in CUSUM for non i.i.d. observations (Tartakovsky et al, 2014;Xie et al, 2021).…”

Section: Cusum Detection Proceduresmentioning

confidence: 94%

Sequential change-point detection for mutually exciting point processes over networks

Wang¹,

Xie²,

Xie

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The overall procedure is summarized in Algorithm 1. Generate m samples from Q k i,ε , k ∈ F defined in (8) and construct the corresponding empirical distribution Qk i,ε .…”

Section: Methodsmentioning

confidence: 99%

“…For any candidate change time t, we treat samples from [t − ω, t − 1] and [t + ω, t] as two groups of observations and solve for the LFDs P * 0 and P * 1 , based on which we calculate the detection statistics as D t = − T (ω t ). We compute the CUSUM-type [8] recursive detection statistic S t = max{0, S t−1 +D t }.…”

Section: B Mnist Digits Classificationmentioning

confidence: 99%

A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets

Wang¹,

Xie²

2022

Preprint

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Hypothesis testing for small-sample scenarios is a practically important problem. In this paper, we investigate the robust hypothesis testing problem in a data-driven manner, where we seek the worst-case detector over distributional uncertainty sets centered around the empirical distribution from samples using Sinkhorn distance. Compared with the Wasserstein robust test, the corresponding least favorable distributions are supported beyond the training samples, which provides a more flexible detector. Various numerical experiments are conducted on both synthetic and real datasets to validate the competitive performances of our proposed method.

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“…The goal is to detect the change of human activity from sequential data. To achieve this goal, we construct a sliding window procedure; the sliding window moves forward with time, and we compute the detection statistic within the sliding window every ten frames; such a procedure can be viewed as the Shewhart Chart in the literature [44]; scanning MMD statistic has been used in [30]. The window size is chosen to be 100, 150, and 200, respectively.…”

Section: Online Human Activity Change-point Detectionmentioning

confidence: 99%

Neural Tangent Kernel Maximum Mean Discrepancy

Cheng,

Xie

2021

Preprint

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We present a novel neural network Maximum Mean Discrepancy (MMD) statistic by identifying a connection between neural tangent kernel (NTK) and MMD statistic. This connection enables us to develop a computationally efficient and memory-efficient approach to compute the MMD statistic and perform neural network based two-sample tests towards addressing the long-standing challenge of memory and computational complexity of the MMD statistic, which is essential for online implementation to assimilate new samples. Theoretically, such a connection allows us to understand the properties of the new test statistic, such as Type-I error and testing power for performing the two-sample test, by leveraging analysis tools for kernel MMD. Numerical experiments on synthetic and real-world datasets validate the theory and demonstrate the effectiveness of the proposed NTK-MMD statistic.

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Sequential (Quickest) Change Detection: Classical Results and New Directions

Cited by 85 publications

References 196 publications

Sequential change-point detection for mutually exciting point processes over networks

Sequential change-point detection for mutually exciting point processes over networks

A Data-Driven Approach to Robust Hypothesis Testing Using Sinkhorn Uncertainty Sets

Neural Tangent Kernel Maximum Mean Discrepancy

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