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September 2013 Spatio-temporal exceedance locations and confidence regions
Joshua P. French, Stephan R. Sain
Ann. Appl. Stat. 7(3): 1421-1449 (September 2013). DOI: 10.1214/13-AOAS631


An exceedance region is the set of locations in a spatial domain where a process exceeds some threshold. Examples of exceedance regions include areas where ozone concentrations exceed safety standards, there is high risk for tornadoes or floods, or heavy-metal levels are dangerously high. Identifying these regions in a spatial or spatio-temporal setting is an important responsibility in environmental monitoring. Exceedance regions are often estimated by finding the areas where predictions from a statistical model exceed some threshold. Even when estimation error is quantifiable at individual locations, the overall estimation error of the estimated exceedance region is still unknown. A method is presented for constructing a confidence region containing the true exceedance region of a spatio-temporal process at a certain time. The underlying latent process and any measurement error are assumed to be Gaussian. Conventional techniques are used to model the spatio-temporal data, and then conditional simulation is combined with hypothesis testing to create the desired confidence region. A simulation study is used to validate the approach for several levels of spatial and temporal dependence. The methodology is used to identify regions of Oregon having high precipitation levels and also used in comparing climate models and assessing climate change using climate models from the North American Regional Climate Change Assessment Program.


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Joshua P. French. Stephan R. Sain. "Spatio-temporal exceedance locations and confidence regions." Ann. Appl. Stat. 7 (3) 1421 - 1449, September 2013.


Published: September 2013
First available in Project Euclid: 3 October 2013

zbMATH: 1283.62226
MathSciNet: MR3127953
Digital Object Identifier: 10.1214/13-AOAS631

Keywords: confidence region , exceedance , Geostatistics , hotspot , spatial statistics

Rights: Copyright © 2013 Institute of Mathematical Statistics


Vol.7 • No. 3 • September 2013
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