Statistiskt seminarium

Abstrakt finns enbart på engelska: Although scoring rules are traditionally aimed at model evaluation, they have also successfully been used for model inference. We propose parameter inference of spatial models through a leave-one-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 matrices, such as spatial Markov models, the approach results in fast computations compared to the likelihood approach. Moreover, the approach allows affecting the robustness to outliers and sensitivity to non-stationarity. Applying the LOOS to ERA5 temperature reanalysis data for the contiguous United States and the average July temperature for the years 1940 to 2023 resulted in estimates with better predictive performance than the maximum likelihood in a fraction of the computation time.