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A new adaptive procedure for multiple window scan statistics

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  • Wu, Tung-Lung
  • Glaz, Joseph

Abstract

Scan statistics have been widely applied to test for unusual cluster of events in many scientific areas. It has been of practical interest on how to select the window size of a scan statistic. An adaptive procedure for multiple window scan statistics is proposed and the distributions are studied for independent identically distributed Bernoulli trials and uniform observations on (0, 1) in one-dimensional case. The idea of the procedure is to select the window sizes sequentially. An initial window size is chosen and the subsequent window sizes are then determined, depending on the value of the current scan statistic at each stage. The power of scan statistics based on the adaptive procedure is compared with power of standard scan statistics. Numerical results and applications for disease clusters detection are given to illustrate our procedure.

Suggested Citation

  • Wu, Tung-Lung & Glaz, Joseph, 2015. "A new adaptive procedure for multiple window scan statistics," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 164-172.
    • Handle: RePEc:eee:csdana:v:82:y:2015:i:c:p:164-172
    DOI: 10.1016/j.csda.2014.09.002
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    References listed on IDEAS

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    1. Karwe, Vatsala V. & Naus, Joseph I., 1997. "New recursive methods for scan statistic probabilities," Computational Statistics & Data Analysis, Elsevier, vol. 23(3), pages 389-402, January.
    2. Joseph I. Naus & Sylvan Wallenstein, 2004. "Multiple Window and Cluster Size Scan Procedures," Methodology and Computing in Applied Probability, Springer, vol. 6(4), pages 389-400, December.
    3. Joseph Glaz & Zhenkui Zhang, 2004. "Multiple Window Discrete Scan Statistics," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(8), pages 967-980.
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    Cited by:

    1. Anthony Cheng & Disheng Mao & Yuping Zhang & Joseph Glaz & Zhengqing Ouyang, 2023. "Translocation detection from Hi‐C data via scan statistics," Biometrics, The International Biometric Society, vol. 79(2), pages 1306-1317, June.
    2. Boutsikas V. Michael & Vaggelatou Eutichia, 2020. "On the Distribution of the Number of Success Runs in a Continuous Time Markov Chain," Methodology and Computing in Applied Probability, Springer, vol. 22(3), pages 969-993, September.
    3. Sotirios Bersimis & Athanasios Sachlas & Pantelis G. Bagos, 2017. "Discriminating membrane proteins using the joint distribution of length sums of success and failure runs," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(2), pages 251-272, June.

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