Oftentimes in many fields of the social and natural sciences, data are obtained within a nested structure (e.g., students within schools). To effectively analyze data with such a structure, multilevel models are frequently employed. The present study utilizes a Monte Carlo simulation to compare several novel multilevel classification algorithms across several varied data conditions for the purpose of prediction. Among these models, the panel neural network and Bayesian generalized mixed effects model (multilevel Bayes) consistently yielded the highest prediction accuracy in test data across nearly all data conditions.

中文翻译：

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混合效应模型的预测：蒙特卡罗模拟研究

在社会科学和自然科学的许多领域中，数据通常是在嵌套结构（例如学校内的学生）内获得的。为了有效地分析具有这种结构的数据，经常采用多级模型。本研究利用蒙特卡罗模拟来比较几种新颖的多级分类算法在几种不同的数据条件下的预测目的。在这些模型中，面板神经网络和贝叶斯广义混合效应模型（多级贝叶斯）在几乎所有数据条件下的测试数据中始终获得最高的预测精度。