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October 1, 2025Open Access

Joint leave-group-out cross-validation in Bayesian spatial models

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This study demonstrates that joint cross-validation improves predictive performance in spatial models, indicating better model selection methods.

Joint scoring in cross-validation reduces variability in predictive estimates, leading to improved model selection.
Experiments on Gaussian covariance structures reveal that aggregation of blocks enhances reliability of predictions.
Focus is given to the influence of correlation structures on cross-validation design, critical for spatial models.
Findings are particularly significant in cases of strong spatial dependence and subtle model differences.

Cooper et al. (2025) studied this question.

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October 1, 2025Open Access

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Authors

This study demonstrates that joint cross-validation improves predictive performance in spatial models, indicating better model selection methods.

Joint scoring in cross-validation reduces variability in predictive estimates, leading to improved model selection.
Experiments on Gaussian covariance structures reveal that aggregation of blocks enhances reliability of predictions.
Focus is given to the influence of correlation structures on cross-validation design, critical for spatial models.
Findings are particularly significant in cases of strong spatial dependence and subtle model differences.

Cooper et al. (2025) studied this question.

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