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April 2010 Optimal and fast detection of spatial clusters with scan statistics
Guenther Walther
Ann. Statist. 38(2): 1010-1033 (April 2010). DOI: 10.1214/09-AOS732


We consider the detection of multivariate spatial clusters in the Bernoulli model with N locations, where the design distribution has weakly dependent marginals. The locations are scanned with a rectangular window with sides parallel to the axes and with varying sizes and aspect ratios. Multivariate scan statistics pose a statistical problem due to the multiple testing over many scan windows, as well as a computational problem because statistics have to be evaluated on many windows. This paper introduces methodology that leads to both statistically optimal inference and computationally efficient algorithms. The main difference to the traditional calibration of scan statistics is the concept of grouping scan windows according to their sizes, and then applying different critical values to different groups. It is shown that this calibration of the scan statistic results in optimal inference for spatial clusters on both small scales and on large scales, as well as in the case where the cluster lives on one of the marginals. Methodology is introduced that allows for an efficient approximation of the set of all rectangles while still guaranteeing the statistical optimality results described above. It is shown that the resulting scan statistic has a computational complexity that is almost linear in N.


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Guenther Walther. "Optimal and fast detection of spatial clusters with scan statistics." Ann. Statist. 38 (2) 1010 - 1033, April 2010.


Published: April 2010
First available in Project Euclid: 19 February 2010

zbMATH: 1183.62076
MathSciNet: MR2604703
Digital Object Identifier: 10.1214/09-AOS732

Primary: 62G10
Secondary: 62H30

Keywords: Bernoulli model , concentration inequality , fast algorithm , multiscale inference , Optimal detection , scan statistic

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.38 • No. 2 • April 2010
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