Principles for statistical inference on big spatio-temporal data from climate models
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ArticleKAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionStatistics Program
KAUST Grant Number
OSR-2015-CRG4-2640Date
2018-02-24Online Publication Date
2018-02-24Print Publication Date
2018-05Permanent link to this record
http://hdl.handle.net/10754/627201
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The 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.Citation
Castruccio 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.Sponsors
This 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).Publisher
Elsevier BVJournal
Statistics & Probability LettersAdditional Links
https://www.sciencedirect.com/science/article/pii/S0167715218300713ae974a485f413a2113503eed53cd6c53
10.1016/j.spl.2018.02.026