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Using Scan Statistics for Cluster Detection: Recognizing Real Bandwagons

Author

Listed:
  • Jie Chen

    (University of Massachusetts)

  • Thomas Ferguson

    (University of Massachusetts
    Institute for New Economic Thinking)

  • Paul Jorgensen

    (The University of Texas Rio Grande Valley)

Abstract

Bandwagons are ubiquitous in social life. No one doubts that people vote at least sometimes for political candidates simply because they are winning and or embrace many fashions simply because they want to “follow the crowd.” But estimating how much a bourgeoning trend owes to pure “bandwagon effects” can be very difficult. Often other factors motivate the people taking action to an unknown degree. In this paper we investigate the use of two variable window scan statistics, the minimum P value scan statistic and the generalized likelihood ratio test (GLRT) statistic, to analyze one important form of the bandwagon problem. We show how these scan statistics can be used to detect the clustering of bandwagon events in a time interval. Once the events are identified, the information can be used to set boundaries on the extent of bandwagoning. The method is illustrated by reference to data on political contributions in the 2016 U.S. Senate elections.

Suggested Citation

  • Jie Chen & Thomas Ferguson & Paul Jorgensen, 2020. "Using Scan Statistics for Cluster Detection: Recognizing Real Bandwagons," Methodology and Computing in Applied Probability, Springer, vol. 22(4), pages 1481-1491, December.
    • Handle: RePEc:spr:metcap:v:22:y:2020:i:4:d:10.1007_s11009-019-09737-1
    DOI: 10.1007/s11009-019-09737-1
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    References listed on IDEAS

    as
    1. Jie Chen & Joseph Glaz, 2016. "Scan statistics for monitoring data modeled by a negative binomial distribution," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 45(6), pages 1632-1642, March.
    2. Thomas Ferguson & Paul Jorgensen & Jie Chen, 2016. "How Money Drives US Congressional Elections," Working Papers Series 48, Institute for New Economic Thinking.
    3. G. Haiman & C. Preda, 2002. "A New Method for Estimating the Distribution of Scan Statistics for a Two-Dimensional Poisson Process," Methodology and Computing in Applied Probability, Springer, vol. 4(4), pages 393-407, December.
    4. Xiao Wang & Joseph Glaz, 2014. "Variable Window Scan Statistics for Normal Data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(10-12), pages 2489-2504, May.
    5. Zhao, Bo & Glaz, Joseph, 2016. "Scan statistics for detecting a local change in variance for normal data with unknown population variance," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 137-145.
    6. Bo Zhao & Joseph Glaz, 2016. "Scan Statistics for Detecting a Local Change in Variance for Normal Data with Known Variance," Methodology and Computing in Applied Probability, Springer, vol. 18(2), pages 563-573, June.
    7. Wang, Xiao & Zhao, Bo & Glaz, Joseph, 2014. "A multiple window scan statistic for time series models," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 196-203.
    8. Joseph Glaz & Zhenkui Zhang, 2004. "Multiple Window Discrete Scan Statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(8), pages 967-980.
    Full references (including those not matched with items on IDEAS)

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