title: False Discovery Control in Large-Scale Spatial Multiple Testing.
abstract: This article develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both point-wise and cluster-wise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate, respectively. A data-driven finite approximation strategy is developed to mimic the oracle procedures on a continuous spatial domain. Our multiple testing procedures are asymptotically valid and can be effectively implemented using Bayesian computational algorithms for analysis of large spatial data sets. Numerical results show that the proposed procedures lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analyzing the time trends in tropospheric ozone in eastern US.
authors: University of Southern California, Los Angeles, USA.
authors: Wenguang
authors: W
authors: Sun
authors: North Carolina State University, Raleigh, USA.
authors: Brian J
authors: BJ
authors: Reich
authors: University of Pennsylvania, Philadelphia, USA.
authors: T Tony
authors: TT
authors: Cai
authors: UT MD Anderson Cancer Center, Houston, USA.
authors: Michele
authors: M
authors: Guindani
authors: Harvard University, Boston, USA.
authors: Armin
authors: A
authors: Schwartzman
keywords: Compound decision theory; false cluster rate; false discovery exceedance; false discovery rate; large-scale multiple testing; spatial dependency
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