JSM 2005 Online Program

Abstract #302343

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Activity Number:	206
Type:	Invited
Date/Time:	Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor:	SSC
Abstract - #302343
Title:	Maximum Scan Score-type Statistics
Author(s):	Joseph Glaz*+ and Zhenkui Zhang
Companies:	University of Connecticut and University of Connecticut
Address:	Department of Statistics, U-4120, Storrs, CT, 06269-4120,
Keywords:	cluster detection ; moving sums ; scan statistic ; variable window
Abstract:	In this presentation, we introduce a maximum scan score-type statistic for testing the null hypothesis that the observations are according to a specified distribution against an alternative that the observations cluster within a window with 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 Bernoulli trials and for uniform observations on the interval [0,1). The advantage in using a maximum scan score-type statistic, over a fixed window scan statistic, is its effectiveness in detecting window-type clustering of observations. Numerical results will be presented to evaluate the performance of this scan statistic. An application in the area of bioinformatics will be discussed.

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