Second-Order Asymptotically Optimal Change-point Detection Algorithm with Sampling Control

Xu, Qunzhi; Mei, Yajun; Moustakides, George V.

doi:10.1109/isit44484.2020.9174114

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…One study develops a second-order asymptotically optimal algorithm when only one location (or stream) is assumed to change. 25 The algorithm samples from a single location until determining there is no change, moves to another location and continues until becoming confident there is no change, eventually cycling through all locations until an alarm is raised. Another study uses TS to sample statistics for each location based on the Shiraev-Roberts procedure.…”

Section: Introductionmentioning

confidence: 99%

Surveillance for endemic infectious disease outbreaks: Adaptive sampling using profile likelihood estimation

Fairley

Rao

Brandeau

et al. 2022

Statistics in Medicine

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Outbreaks of an endemic infectious disease can occur when the disease is introduced into a highly susceptible subpopulation or when the disease enters a network of connected individuals. For example, significant HIV outbreaks among people who inject drugs have occurred in at least half a dozen US states in recent years. This motivates the current study: how can limited testing resources be allocated across geographic regions to rapidly detect outbreaks of an endemic infectious disease? We develop an adaptive sampling algorithm that uses profile likelihood to estimate the distribution of the number of positive tests that would occur for each location in a future time period if that location were sampled. Sampling is performed in the location with the highest estimated probability of triggering an outbreak alarm in the next time period. The alarm function is determined by a semiparametric likelihood ratio test. We compare the profile likelihood sampling (PLS) method numerically to uniform random sampling (URS) and Thompson sampling (TS). TS was worse than URS when the outbreak occurred in a location with lower initial prevalence than other locations. PLS had lower time to outbreak detection than TS in some but not all scenarios, but was always better than URS even when the outbreak occurred in a location with a lower initial prevalence than other locations. PLS provides an effective and reliable method for rapidly detecting endemic disease outbreaks that is robust to this uncertainty.

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Section: Introductionmentioning

confidence: 99%

Surveillance for endemic infectious disease outbreaks: Adaptive sampling using profile likelihood estimation

Fairley

Rao

Brandeau

et al. 2022

Statistics in Medicine

View full text Add to dashboard Cite

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Asymptotic optimality theory for active quickest detection with unknown postchange parameters

Mei

2023

Sequential Analysis

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Sequential Change Detection of a Correlation Structure under a Sampling Constraint

Chaudhuri

Fellouris

Tajer

2021

2021 IEEE International Symposium on Information Theory (ISIT)

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This paper considers the problem of sequentially detecting a change in the joint distribution of multiple data sources under a sampling constraint. Specifically, the channels or sources generate observations that are independent over time, but not necessarily independent at any given time instant. The sources follow an initial joint distribution, and at an unknown time instant, the joint distribution of an unknown subset of sources changes. Importantly, there is a hard constraint that only a fixed number of sources are allowed to be sampled at each time instant. The goal is to sequentially observe the sources according to the constraint, and stop sampling as quickly as possible after the change while controlling the false alarm rate below a user-specified level. The sources can be selected dynamically based on the already collected data, and thus, a policy for this problem consists of a joint sampling and change-detection rule. A non-randomized policy is studied, and an upper bound is established on its worst-case conditional expected detection delay with respect to both the change point and the observations from the affected sources before the change. It is shown that, in certain cases, this rule achieves first-order asymptotic optimality as the false alarm rate tends to zero, simultaneously under every possible post-change distribution and among all schemes that satisfy the same sampling and false alarm constraints. These general results are subsequently applied to the problems of (i) detecting a change in the marginal distributions of (not necessarily independent) information sources, and (ii) detecting a change in the covariance structure of Gaussian information sources.

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Second-Order Asymptotically Optimal Change-point Detection Algorithm with Sampling Control

Cited by 3 publications

References 8 publications

Surveillance for endemic infectious disease outbreaks: Adaptive sampling using profile likelihood estimation

Surveillance for endemic infectious disease outbreaks: Adaptive sampling using profile likelihood estimation

Asymptotic optimality theory for active quickest detection with unknown postchange parameters

Sequential Change Detection of a Correlation Structure under a Sampling Constraint

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