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 …

Abstract Machine learning has become popular in ecology but its use has remained
restricted to predicting, rather than understanding, the natural world. Many researchers …

Background Machine learning methodologies are gaining popularity for developing medical
prediction models for datasets with a large number of predictors, particularly in the setting of …

Objective: Decision-tree methods are machine-learning methods which provide results that
are relatively easy to interpret and apply by human decision makers. The resulting decision …

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 …

This paper aims to enhance the prediction accuracy of hydrogen solubility in aqueous
solution, which is crucial for safe and efficient underground hydrogen storage (UHS). The …

Latent Gaussian models and boosting are widely used techniques in statistics and machine
learning. Tree-boosting shows excellent prediction accuracy on many data sets, but …

Clustered binary outcomes and datasets with many predictor variables are frequently
encountered in clinical research (eg longitudinal studies). Generalized linear mixed models …

Cardiovascular disease (CVD) can often lead to serious consequences such as death or
disability. This study aims to identify a tree-based machine learning method with the best …

There has been growing interest in using nonparametric machine learning approaches for
propensity score estimation in order to foster robustness against misspecification of the …