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Abstract:
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With the development of observing techniques and computing devices, it has become easier and more common to obtain large datasets. Statistical inference in spatial statistics becomes computationally challenging. For decades, various approximation methods have been proposed to model and analyze large-scale spatial data when the exact computation is infeasible. However, in the literature, the performance of the statistical inference using those proposed approximation methods was usually assessed with small and medium datasets only, for which the exact solution can be obtained. Therefore, we organized a competition, KAUST Competition on Spatial Statistics for Large Datasets, in which we generate simulated datasets with a million spatial locations with the help of the ExaGeoStat software. Because we know the true process, the assessment of approximation methods focuses not only on predictions but also on the model inference. About 30 research teams worldwide participated in this competition. In this talk, I will give an overview of the competition and discuss the results by different methods with a brief comparison.
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