Statistics and Its Interface

Volume 6 (2013)

Number 1

Spatial multiresolution cluster detection method

Pages: 65 – 77

DOI: https://dx.doi.org/10.4310/SII.2013.v6.n1.a7

Authors

Lingsong Zhang (Department of Statistics, Purdue University, West Lafayette, Indiana, U.S.A.)

Zhengyuan Zhu (Department of Statistics & Statistical Laboratory, Iowa State University, Ames, Ia., U.S.A.)

Abstract

A novel multi-resolution cluster detection (MCD) method is proposed to identify irregularly shaped clusters in space. Multi-scale test statistic on a single cell is derived based on likelihood ratio statistic for Bernoulli sequence, Poisson sequence and Normal sequence. A neighborhood variability measure is defined to select the optimal test threshold. The MCD method is compared with single scale testing methods controlling for false discovery rate and the spatial scan statistics using simulation and f-MRI data. The MCD method is shown to be more effective for discovering irregularly shaped clusters, and the implementation of this method does not require heavy computation, making it suitable for cluster detection for large spatial data.

Keywords

multiresolution, scan statistics, scale space inference, spatial test

2010 Mathematics Subject Classification

60K35

Full Text (PDF format)

Published 18 March 2013