The following pages link to randomSurvivalForest (Q20146):
Displaying 42 items.
- The benefit of data-based model complexity selection via prediction error curves in time-to-event data (Q63246) (← links)
- Random survival forests (Q97881) (← links)
- Boosted multivariate trees for longitudinal data (Q113262) (← links)
- Boosted coefficient models (Q693319) (← links)
- Bayesian Weibull tree models for survival analysis of clinico-genomic data (Q713835) (← links)
- Root- estimability of some missing data models (Q765837) (← links)
- Rationale and applications of survival tree and survival ensemble methods (Q888036) (← links)
- Navigating random forests and related advances in algorithmic modeling (Q975577) (← links)
- Consistency of random survival forests (Q979192) (← links)
- An integrative pathway-based clinical-genomic model for cancer survival prediction (Q988098) (← links)
- \(L_1\) splitting rules in survival forests (Q1641909) (← links)
- Ensemble survival tree models to reveal pairwise interactions of variables with time-to-events outcomes in low-dimensional setting (Q1672811) (← links)
- A review of survival trees (Q1950331) (← links)
- Discrete-time survival forests with Hellinger distance decision trees (Q1987191) (← links)
- Representative random sampling: an empirical evaluation of a novel bin stratification method for model performance estimation (Q2103978) (← links)
- Nonparametric feature selection by random forests and deep neural networks (Q2129580) (← links)
- Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors (Q2172011) (← links)
- Random forest with acceptance-rejection trees (Q2203396) (← links)
- Oblique random survival forests (Q2281237) (← links)
- The effect of splitting on random forests (Q2347711) (← links)
- Variable importance in binary regression trees and forests (Q2426816) (← links)
- A random forest based approach for predicting spreads in the primary catastrophe bond market (Q2665850) (← links)
- Stochastic modelling of mineral exploration targets (Q2675147) (← links)
- (Q3116053) (← links)
- A fast adaptive Lasso for the cox regression via safe screening rules (Q3389652) (← links)
- Novel Aggregate Deletion/Substitution/Addition Learning Algorithms for Recursive Partitioning (Q3391141) (← links)
- Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods (Q3391146) (← links)
- A Modified Random Survival Forests Algorithm for High Dimensional Predictors and Self-Reported Outcomes (Q3391157) (← links)
- Random survival forests for high‐dimensional data (Q4969754) (← links)
- Bayesian survival trees for clustered observations, applied to tooth prognosis (Q4969929) (← links)
- Tree aggregation for random forest class probability estimation (Q4970316) (← links)
- Predicting risk for adverse health events using random forest (Q5036398) (← links)
- Regression of survival data via twin support vector regression (Q5042113) (← links)
- Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models (Q5057087) (← links)
- <i>L</i><sub>0</sub>-Regularized Learning for High-Dimensional Additive Hazards Regression (Q5058017) (← links)
- High dimensional variable selection with clustered data: an application of random multivariate survival forests for detection of outlier medical device components (Q5107399) (← links)
- Shortcomings of Transfer Entropy and Partial Transfer Entropy: Extending Them to Escape the Curse of Dimensionality (Q5148912) (← links)
- Automatic model selection for high-dimensional survival analysis (Q5220703) (← links)
- Censoring Unbiased Regression Trees and Ensembles (Q5229919) (← links)
- High-Dimensional Variable Selection for Survival Data (Q5254949) (← links)
- The Impact of Churn on Client Value in Health Insurance, Evaluation Using a Random Forest Under Various Censoring Mechanisms (Q5881989) (← links)
- Publication:5042113 (← links)
