Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection-Reference-Cited by-同舟云学术

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Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

Published:2022-09-03 Issue: Volume: Page:1-25
ISSN:0266-4763
Container-title:Journal of Applied Statistics
language:en
Short-container-title:Journal of Applied Statistics

Author:

Hu Jiuyun¹,Mei Yajun²^ORCID,Holte Sarah³^ORCID,Yan Hao¹^ORCID

Affiliation:

1. School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA

2. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA

3. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

Funder

NSF

NIH

Publisher

Informa UK Limited

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://www.tandfonline.com/doi/pdf/10.1080/02664763.2022.2117288

Reference42 articles.

1. Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base

2. Change-point detection in high-dimensional covariance structure

3. Optimal allocation of limited test resources for the quantification of COVID-19 infections

4. S3T: A score statistic for spatiotemporal change point detection

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1. A novel ensemble deep reinforcement learning model for short‐term load forecasting based on Q‐learning dynamic model selection;The Journal of Engineering;2024-07

2. Editorial to the special issue: modern streaming data analytics;Journal of Applied Statistics;2023-10-05

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