Maximum scan score-type statistics

Joseph Glaz and Zhenkui Zhang

Statistics & Probability Letters, 2006, vol. 76, issue 13, 1316-1322

Abstract: In this article we introduce a maximum scan score-type statistic for testing the null hypothesis that the observations are iid according to a specified distribution, against an alternative that the observations cluster within a window of unknown length. This statistic is a variable window scan statistic, based on a finite number of standardized fixed window scan statistics. Approximations for the significance level of this statistic are derived for 0-1 iid Bernoulli trials and for iid uniform observations on the interval [0,1). The advantage in using a maximum scan score-type statistic, rather than a single fixed window scan statistic, is that it is more effective in detecting window-type clustering of observations.

Keywords: Clustering; detection; Bonferroni-type; inequality; Moving; sums; Scan; statistic; Variable; window (search for similar items in EconPapers)
Date: 2006
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Citations: View citations in EconPapers (2)

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