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.

翻译：评估模型拟合后的预测性能至关重要，留一交叉验证是一种标准方法。然而，对于包含多种预测任务的结构化模型，该方法往往信息不足。作为解决方案，留组交叉验证是一种扩展方法，其中留出的组可适应不同的预测任务。本文提出一种用于留组交叉验证的自动组构建流程，以在预测任务未明确时估计预测性能。我们还针对潜在高斯模型提出了一种留组交叉验证的高效近似方法。上述两种流程均在R-INLA软件中实现。