Collection
Special Issue: Computational Spatial Statistics for Large Datasets in Environmental Problems
Spatial datasets increasingly contain many different sorts of data, from topographical, geometric or geographic information, as it is the case of environmental data, comprising measurements taken across a huge number of locations. The development of advanced observation techniques has led to increasingly larger datasets with high dimensionality, making statistical inference in spatial statistics computationally challenging and very costly. Various approximation methods can be used to model and analyze these large real-world spatial datasets, where exact computation is no longer feasible and inference is typically validated empirically or via prediction accuracy with the fitted model. In this context, geospatial applications have brought High-Performance Computing (HPC) into the mainstream and further increased its use in the spatial statistics field. ExaGeoStat is one such example of an HPC software that enables large-scale parallel generation, modeling, and prediction of large geospatial data via covariance matrices. Summing up, combining nuanced statistical methods with a robust parallel computational platform has enabled a modeling scheme that better predicts environmental conditions while being efficient enough to cover millions of monitoring locations.
Taking advantage of the 2023 KAUST Competition on Spatial Statistics for Large Datasets, as a follow-up of the two previous ones in 2021 and 2022, the Journal of Agricultural, Biological and Environmental Statistics (JABES) is calling for a special issue related to the theme Computational Spatial Statistics for Large Datasets. Although we very much encourage the participants (winners and not winners) of this KAUST competition to submit their papers to this special issue, we emphasize this is an open call for any researcher working on this field.
Call for Papers Flyer: Computational Spatial Statistics for Large Datasets in Environmental Problems
Editors
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Jorge Mateu
Dr. Jorge Mateu earned a bachelor's degree in 1992 from the University of Valencia (Spain) in Mathematics and Statistics, and completed his PhD in Statistics in 1998 from the same university under the supervision of Peter Diggle (Lancaster University, UK) and Francisco Montes (UV, Spain). Dr. Mateu is currently full professor of Statistics with the Department of Mathematics at University Jaume I of Castellon (Spain).
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Joe Guinness
Dr. Joe Guinness is an assistant professor in Cornell University's Department of Statistics and Data Science. He studies modeling and computational issues that arise in the analysis of large spatial-temporal datasets, with a focus on applications in earth sciences, including soil, weather, and climate. He teaches a graduate course in spatial statistics.
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Andy Poppick
Dr. Andy Poppick is an Assistant Professor in the Department of Mathematics and Statistics at Carleton College.
