Journal
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
Volume 77, Issue 1, Pages 59-83Publisher
WILEY
DOI: 10.1111/rssb.12064
Keywords
Compound decision theory; False cluster rate; False discovery exceedance; False discovery rate; Large-scale multiple testing; Spatial dependence
Categories
Funding
- National Science Foundation [DMS-CAREER 1255406, DMS-1244556, DMS-1208982]
- US Environmental Protection Agency [R835228, 1107046]
- National Institutes of Health [5R01ES014843-02, R21ES022795-01A1, R01 CA 127334, R01CA157528]
- National Science Foundation 'Focused research groups' grant [DMS-0854973]
- National Institutes of Health-National Cancer Institute [P30CA016672]
- NATIONAL CANCER INSTITUTE [P30CA016672] Funding Source: NIH RePORTER
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The paper develops a unified theoretical and computational framework for false discovery control in multiple testing of spatial signals. We consider both pointwise and clusterwise spatial analyses, and derive oracle procedures which optimally control the false discovery rate, false discovery exceedance and false cluster rate. 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 procedures proposed lead to more accurate error control and better power performance than conventional methods. We demonstrate our methods for analysing the time trends in tropospheric ozone in eastern USA.
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