In longitudinal studies variables are measured repeatedly over time, leading to clustered
and correlated observations. If the goal of the study is to develop prediction models …

Random forests are one of the state-of-the-art supervised machine learning methods and
achieve good performance in high-dimensional settings where p, the number of predictors …

This paper presents an extension of the standard regression tree method to clustered data.
Previous works extending tree methods to accommodate correlated data are mainly based …

In recent years, the popularity of procedures for collecting intensive longitudinal data, such
as the experience-sampling method, has increased greatly. The data collected using such …

Abstract Background and goal The Random Forest (RF) algorithm for regression and
classification has considerably gained popularity since its introduction in 2001. Meanwhile, it …

Identifying genetic variants associated with complex disease in high‐dimensional data is a
challenging problem, and complicated etiologies such as gene–gene interactions are often …

Random forests have emerged as a versatile and highly accurate classification and
regression methodology, requiring little tuning and providing interpretable outputs. Here, we …

The statistical analysis of repeated measures data over time can be a remarkably
challenging task that, if successful, has the potential for allowing significant insight into many …

Longitudinal data refer to the situation where repeated observations are available for each
sampled object. Clustered data, where observations are nested in a hierarchical structure …

With contributions from some of the most prominent researchers in the field, this carefully
edited collection provides a clear, comprehensive, and unified overview of recent …