IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v206y2024ics0167715223002298.html
   My bibliography  Save this article

A Bayesian spatial scan statistic for multinomial data

Author

Listed:
  • Self, Stella
  • Nolan, Melissa

Abstract

Spatial scan statistics are commonly used to detect clustering. We present a Bayesian spatial scan statistic for multinomial data. After validating our method with a simulation study, we use it to detect clusters of SARS-CoV-2 infection/immunity in South Carolina.

Suggested Citation

  • Self, Stella & Nolan, Melissa, 2024. "A Bayesian spatial scan statistic for multinomial data," Statistics & Probability Letters, Elsevier, vol. 206(C).
    • Handle: RePEc:eee:stapro:v:206:y:2024:i:c:s0167715223002298
    DOI: 10.1016/j.spl.2023.110005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715223002298
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2023.110005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kelter, Riko, 2022. "Power analysis and type I and type II error rates of Bayesian nonparametric two-sample tests for location-shifts based on the Bayes factor under Cauchy priors," Computational Statistics & Data Analysis, Elsevier, vol. 165(C).
    2. Wang, Tai-Chi & Phoa, Frederick Kin Hing, 2016. "A scanning method for detecting clustering pattern of both attribute and structure in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 445(C), pages 295-309.
    3. Zhou, Ruoyu & Shu, Lianjie & Su, Yan, 2015. "An adaptive minimum spanning tree test for detecting irregularly-shaped spatial clusters," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 134-146.
    4. Costa, Marcelo Azevedo & Assunção, Renato Martins & Kulldorff, Martin, 2012. "Constrained spanning tree algorithms for irregularly-shaped spatial clustering," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1771-1783.
    5. Lan Huang & Martin Kulldorff & David Gregorio, 2007. "A Spatial Scan Statistic for Survival Data," Biometrics, The International Biometric Society, vol. 63(1), pages 109-118, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ali Abolhassani & Marcos O. Prates & Safieh Mahmoodi, 2023. "Irregular Shaped Small Nodule Detection Using a Robust Scan Statistic," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 141-162, April.
    2. Inkyung Jung, 2019. "Spatial scan statistics for matched case-control data," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-10, August.
    3. Smida, Zaineb & Cucala, Lionel & Gannoun, Ali & Durif, Ghislain, 2022. "A Wilcoxon-Mann-Whitney spatial scan statistic for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 167(C).
    4. Mohamed-Salem Ahmed & Lionel Cucala & Michaël Genin, 2021. "Spatial autoregressive models for scan statistic," Journal of Spatial Econometrics, Springer, vol. 2(1), pages 1-20, December.
    5. Xiaojuan Qi & Jingxian Guo & Shenjun Yao & Ting Liu & Hao Hou & Huan Ren, 2023. "Comprehensive Dynamic Influence of Multiple Meteorological Factors on the Detection Rate of Bacterial Foodborne Diseases under Spatio-Temporal Heterogeneity," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    6. Andrea J. Cook & Yi Li & David Arterburn & Ram C. Tiwari, 2010. "Spatial Cluster Detection for Weighted Outcomes Using Cumulative Geographic Residuals," Biometrics, The International Biometric Society, vol. 66(3), pages 783-792, September.
    7. Shino Shiode & Narushige Shiode, 2022. "Network-Based Space-Time Scan Statistics for Detecting Micro-Scale Hotspots," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    8. Frederick Kin Hing Phoa & Hsin-Yi Lai & Livia Lin-Hsuan Chang & Keisuke Honda, 2020. "A two-step deep learning approach to data classification and modeling and a demonstration on subject type relationship analysis in the Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 851-863, November.
    9. Daniel Commenges & Benoit Liquet, 2008. "Asymptotic Distribution of Score Statistics for Spatial Cluster Detection with Censored Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1287-1289, December.
    10. Wei Wang & Sheng Li & Tao Zhang & Fei Yin & Yue Ma, 2023. "Detecting the spatial clustering of exposure–response relationships with estimation error: a novel spatial scan statistic," Biometrics, The International Biometric Society, vol. 79(4), pages 3522-3532, December.
    11. Liu, Ying & Liu, Yawen & Zhang, Tonglin, 2018. "Wald-based spatial scan statistics for cluster detection," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 298-310.
    12. Lee, Myeonggyun & Jung, Inkyung, 2019. "Modified spatial scan statistics using a restricted likelihood ratio for ordinal outcome data," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 28-39.
    13. Margherita Silan & Pietro Belloni & Giovanna Boccuzzo, 2023. "Identification of neighborhood clusters on data balanced by a poset-based approach," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(4), pages 1295-1316, October.
    14. Zhanjun He & Rongqi Lai & Zhipeng Wang & Huimin Liu & Min Deng, 2022. "Comparative Study of Approaches for Detecting Crime Hotspots with Considering Concentration and Shape Characteristics," IJERPH, MDPI, vol. 19(21), pages 1-16, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:206:y:2024:i:c:s0167715223002298. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.