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"Modeling Big, Heterogeneous, Non-Gaussian Spatial and Spatio-Temporal " by Matthew Sainsbury-Dale, Andrew Zammit- Mangion et al.

Non-Gaussian spatial and spatio-temporal data are becoming increasingly prevalent, and their analysis is needed in a variety of disciplines. FRK is an R package for spatial and spatio-temporal modeling and prediction with very large data sets that, to date, has only supported linear process models and Gaussian data models. In this paper, we describe a major upgrade to FRK that allows for non-Gaussian data to be analyzed in a generalized linear mixed model framework. These vastly more general spatial and spatio-temporal models are fitted using the Laplace approximation via the software TMB. The existing functionality of FRK is retained with this advance into non-Gaussian models; in particular, it allows for automatic basis-function construction, it can handle both point-referenced and areal data simultaneously, and it can predict process values at any spatial support from these data. This new version of FRK also allows for the use of a large number of basis functions when modeling the s ....

Areal Data , Basis Functions , Big Data , Hange Of Support , Ixed Rank Kriging , On Gaussian Data , Spatial Statistics ,

King's College Gets £80M for Revolutionary AI Tech Research

King's College Gets £80M for Revolutionary AI Tech Research
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City Of , United Kingdom , Charlotte Deane , Hana Chockler , University Of Edinburgh , Sciences Research Council , Department Of Informatics , School Of Cancer , Pharmaceutical Sciences , Areal Data , Physical Sciences Research Council , Computer Science , Executive Chair ,