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：标准技术如留一交叉验证（LOOCV）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。在此类情况下，训练集与测试集之间的相关性可能对模型预测误差产生显著影响。为克服这一问题，一种针对留组交叉验证（LGOCV）的自动组构建程序近期成为提升结构化模型预测性能评估的实用工具。本文旨在：(i) 比较结构化模型中LOOCV与LGOCV在模型选择与预测性能方面的表现；(ii) 通过使用INLA拟合的复杂结构化模型在空间统计中的实际数据应用，展示自动LGOCV方法的实用性。首先，我们简要回顾近期提出的潜在高斯模型中自动组构建的LGOCV方法关键要点。继而，通过模拟时间与空间数据分析中的外推任务，证明该方法在筛选具有最高预测性能模型方面的有效性。最后，我们阐述LGOCV方法在建模复杂结构化数据（包括时空多元计数数据、空间组成数据及时空间地理数据）中的有效性。