Randomized Repeated Significance Tests Based on Scan Statistics for Discrete Data

Yong Qiao () and Joseph Glaz ()
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Yong Qiao: University of Connecticut
Joseph Glaz: University of Connecticut

Methodology and Computing in Applied Probability, 2025, vol. 27, issue 1, 1-26

Abstract: Abstract In this article we introduce randomized repeated significance tests (RSTs), based on scan statistics, for detecting a local change in a parameter of a distribution for one and two dimensional discrete data. When the size of the window where a local change in the parameter has occurred is known, a randomized RST based on a fixed window scan statistic is proposed. When the size of the window where a local change in the parameter has occurred is unknown, a randomized RST based on the minimum P-value scan statistic is developed. Simulation based methods are used to implement these randomized RSTs. Numerical results for one and two dimensional data, generated from Bernoulli and Poisson distributions, for selected values of model parameters, demonstrate the effectiveness of the randomized RSTs in detecting a local change in the parameter of the respective model.

Keywords: Minimum P-Value statistic; Multiple window scan statistic; Non-parametric Bootstrap; Sequential analysis; Local changed detection; Randomized test; 60G40; 62E17; 62H11; 62L10 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11009-024-10132-8

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