All rights reservedFu, LiyaWang, You-GanWu, Jinran2025-10-1720242024-06-130169-716110.1016/bs.host.2023.10.007https://hdl.handle.net/20.500.14802/14022This work aims to provide a review of methodology on analysis of longitudinal data focusing on (i) how to select different model components: the covariance (correlation and variance) functions or structures, and the predictive variables; (ii) the robust approaches including rank and quantile regression; and (iii) machine learning algorithms that incorporate the temporal or clustering effects. Specifically, among longitudinal machine learning algorithms, tree-based methods are widely used for modeling random effects, while support vector machine-based techniques are adapted to include temporal structure and random effects. More recently, there has been an emerging interest in using (deep) neural networks trained with derived optimization objectives to capture complex patterns in longitudinal data.Correlation information criterionCovariance modelingData-driven tuning parameterMachine learningModel selectionPenalty functionQuantile regressionRank regressionRobust estimationWorking likelihoodJournal articleControlledPUB0201098506