Using Scan Statistics for Cluster Detection: Recognizing Real Bandwagons

Jie Chen (), Thomas Ferguson and Paul Jorgensen
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Jie Chen: University of Massachusetts
Thomas Ferguson: University of Massachusetts
Paul Jorgensen: The University of Texas Rio Grande Valley

Methodology and Computing in Applied Probability, 2020, vol. 22, issue 4, 1481-1491

Abstract: 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.

Keywords: Bandwagon; Likelihood ratio test; Minimum p value statistic; Simulation studies; Testing homogeneity; Scan statistics; Variable window (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s11009-019-09737-1

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