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Found 8 papers, 2 papers with code
Random Forests for time-fixed and time-dependent predictors: The DynForest R package
The R package DynForest implements random forests for predicting a continuous, a categorical or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent predictors.
Random survival forests with multivariate longitudinal endogenous covariates
The individual event probability is estimated in each tree by the Aalen-Johansen estimator of the leaf in which the subject is classified according to his/her history of predictors.
Individual dynamic prediction of clinical endpoint from large dimensional longitudinal biomarker history: a landmark approach
We then applied the methodology in two prediction contexts: a clinical context with the prediction of death for patients with primary biliary cholangitis, and a public health context with the prediction of death in the general elderly population at different ages.
Fréchet random forests for metric space valued regression with non euclidean predictors
Random forests are a statistical learning method widely used in many areas of scientific research because of its ability to learn complex relationships between input and output variables and also its capacity to handle high-dimensional data.
Combining clustering of variables and feature selection using random forests
Numerical performances of the proposed approach are compared with direct applications of random forests and variable selection using random forests on the original p variables.
Statistics Theory Statistics Theory
Comments on: "A Random Forest Guided Tour" by G. Biau and E. Scornet
This paper is a comment on the survey paper by Biau and Scornet (2016) about random forests.
Random Forests for Big Data
They are a powerful nonparametric statistical method allowing to consider in a single and versatile framework regression problems, as well as two-class and multi-class classification problems.
Analysis of purely random forests bias
Under some regularity assumptions on the regression function, we show that the bias of an infinite forest decreases at a faster rate (with respect to the size of each tree) than a single tree.
