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.
Publication Year: 2006
Publication Date: 2006-07-01
Language: en
Type: article
Indexed In: ['crossref']
Access and Citation
Cited By Count: 15
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot