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SUMMARY:Helga Olafsdottir (Chalmers University of Technology & University 
 of Gothenburg)
DTSTART:20240821T111500Z
DTEND:20240821T120000Z
DTSTAMP:20260422T155325Z
UID:gbgstats/62
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/gbgstats/62/
 ">Scoring rule inference for spatial statistics based on cross-validation<
 /a>\nby Helga Olafsdottir (Chalmers University of Technology & University 
 of Gothenburg) as part of Gothenburg statistics seminar\n\nLecture held in
  MVF21 (sic!).\n\nAbstract\nAlthough scoring rules are traditionally aimed
  at model evaluation\, they have also successfully been used for model inf
 erence. We propose parameter inference of spatial models through a leave-o
 ne-out cross-validation approach (LOOS)\, where the predictive ability is 
 optimised instead of the likelihood. The approach is studied for different
  Gaussian spatial models. For Gaussian models with sparse precision matric
 es\, such as spatial Markov models\, the approach results in fast computat
 ions compared to the likelihood approach. Moreover\, the approach allows a
 ffecting the robustness to outliers and sensitivity to non-stationarity. A
 pplying the LOOS to ERA5 temperature reanalysis data for the contiguous Un
 ited States and the average July temperature for the years 1940 to 2023 re
 sulted in estimates with better predictive performance than the maximum li
 kelihood in a fraction of the computation time.\n
LOCATION:https://researchseminars.org/talk/gbgstats/62/
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