Principles for statistical inference on big spatio-temporal data from climate models
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberOSR-2015-CRG4-2640
Online Publication Date2018-02-24
Print Publication Date2018-05
Permanent link to this recordhttp://hdl.handle.net/10754/627201
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AbstractThe vast increase in size of modern spatio-temporal datasets has prompted statisticians working in environmental applications to develop new and efficient methodologies that are still able to achieve inference for nontrivial models within an affordable time. Climate model outputs push the limits of inference for Gaussian processes, as their size can easily be larger than 10 billion data points. Drawing from our experience in a set of previous work, we provide three principles for the statistical analysis of such large datasets that leverage recent methodological and computational advances. These principles emphasize the need of embedding distributed and parallel computing in the inferential process.
CitationCastruccio S, Genton MG (2018) Principles for statistical inference on big spatio-temporal data from climate models. Statistics & Probability Letters. Available: http://dx.doi.org/10.1016/j.spl.2018.02.026.
SponsorsThis publication is based upon work supported by King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No: OSR-2015-CRG4-2640. Fig. 1 was produced by Heno Hwang, scientific illustrator at King Abdullah University of Science and Technology (KAUST).
JournalStatistics & Probability Letters