Standard techniques such as leave-one-out cross-validation (LOOCV) might not be suitable for evaluating the predictive performance of models incorporating structured random effects. In such cases, the correlation between the training and test sets could have a notable impact on the model's prediction error. To overcome this issue, an automatic group construction procedure for leave-group-out cross validation (LGOCV) has recently emerged as a valuable tool for enhancing predictive performance measurement in structured models. The purpose of this paper is (i) to compare LOOCV and LGOCV within structured models, emphasizing model selection and predictive performance, and (ii) to provide real data applications in spatial statistics using complex structured models fitted with INLA, showcasing the utility of the automatic LGOCV method. First, we briefly review the key aspects of the recently proposed LGOCV method for automatic group construction in latent Gaussian models. We also demonstrate the effectiveness of this method for selecting the model with the highest predictive performance by simulating extrapolation tasks in both temporal and spatial data analyses. Finally, we provide insights into the effectiveness of the LGOCV method in modelling complex structured data, encompassing spatio-temporal multivariate count data, spatial compositional data, and spatio-temporal geospatial data.

翻译：留一法交叉验证（LOOCV）等标准技术可能不适用于评估包含结构化随机效应的模型的预测性能。在这种情况下，训练集与测试集之间的相关性可能对模型的预测误差产生显著影响。为解决这一问题，一种用于留组交叉验证（LGOCV）的自动分组构建程序近期已成为增强结构化模型预测性能评估的有效工具。本文旨在：(i) 在结构化模型中比较LOOCV与LGOCV，重点关注模型选择与预测性能；(ii) 通过使用INLA拟合的复杂结构化模型，提供空间统计中的真实数据应用案例，展示自动LGOCV方法的实用性。首先，我们简要回顾了近期提出的面向潜在高斯模型的自动分组构建LGOCV方法的关键要素。同时，通过模拟时间与空间数据分析中的外推任务，我们验证了该方法在筛选具有最高预测性能模型方面的有效性。最后，我们深入探讨了LGOCV方法在复杂结构化数据建模中的效能，涵盖时空多元计数数据、空间成分数据及时空地理空间数据等应用场景。