Leave-group-out cross-validation for latent Gaussian models

Evaluating predictive performance is essential after fitting a model and leave-one-out cross-validation is a standard method. However, it is often not informative for a structured model with many possible prediction tasks. As a solution, leave-group-out cross-validation is an extension where the left-out-groups adapt to different prediction tasks. In this paper, we propose an automatic group construction procedure for leave-group-out cross-validation to estimate the predictive performance when the prediction task is not specified. We also propose an efficient approximation of leave-group-out cross-validation for latent Gaussian models. We implement both procedures in the R-INLA software.