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Wilcoxon Rank Sum Scan Statistics for Continuous Data with Outliers

In: Handbook of Scan Statistics

Author

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  • Qianzhu Wu

    (John Hancock Financial)

  • Joseph Glaz

    (University of Connecticut, Department of Statistics)

Abstract

In this chapter, we investigate the performance of several Wilcoxon rank sum scan statistics in detecting a local change in population mean, in the presence of outliers, for one- and two-dimensional data, generated by a continuous distribution. The detection problem is formulated via testing of hypotheses and implemented via simulation using a nonparametric bootstrap approach. The performance of the Wilcoxon rank sum scan statistics discussed in this chapter is evaluated via simulation based on the accuracy of achieving the specified significance level and the power against selected alternatives. The selected alternative hypotheses are based on probability models for the observed data, probability models for the outliers, and their location in the data and selected parameters indicating the local change in the population mean. Directions for future research are discussed as well in this chapter.

Suggested Citation

  • Qianzhu Wu & Joseph Glaz, 2024. "Wilcoxon Rank Sum Scan Statistics for Continuous Data with Outliers," Springer Books, in: Joseph Glaz & Markos V. Koutras (ed.), Handbook of Scan Statistics, chapter 40, pages 795-813, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-8033-4_67
    DOI: 10.1007/978-1-4614-8033-4_67
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