We propose a new statistical method, called generalized mixed‐effects random forest (GMERF), that extends the use of random forest to the analysis of hierarchical data, for any type of response variable in the exponential family. The method maintains the flexibility and the ability of modeling complex patterns within the data, typical of tree‐based ensemble methods, and it can handle both continuous and discrete covariates. At the same time, GMERF takes into account the nested structure of hierarchical data, modeling the dependence structure that exists at the highest level of the hierarchy and allowing statistical inference on this structure. In the case study, we apply GMERF to Higher Education data to analyze the university student dropout phenomenon. We predict engineering student dropout probability by means of student‐level information and considering the degree program students are enrolled in as grouping factor.

中文翻译：

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广义混合效应随机森林：一种预测大学生辍学的灵活方法

我们提出了一种新的统计方法，称为广义混合效应随机森林（GMERF），该方法将对指数族中任何类型的响应变量的随机森林的使用扩展到层次数据的分析中。该方法保持了数据中复杂模式建模的灵活性和能力，这是基于树的集成方法的典型特性，并且可以处理连续协变量和离散协变量。同时，GMERF考虑了层次结构数据的嵌套结构，对存在于层次结构最高级别的依赖结构进行了建模，并允许对该结构进行统计推断。在案例研究中，我们将GMERF应用于高等教育数据来分析大学生辍学现象。