The following pages link to Random survival forests (Q97881):
Displaying 37 items.
- Inference for Treatment-Specific Survival Curves Using Machine Learning (Q6567948) (← links)
- Pseudo-value regression trees (Q6571296) (← links)
- Hierarchical nonparametric survival modeling for demand forecasting with fragmented categorical covariates (Q6574624) (← links)
- Applying machine learning techniques in survival analysis to the private pension system in Turkey (Q6579740) (← links)
- Nonparametric failure time: time-to-event machine learning with heteroskedastic Bayesian additive regression trees and low information omnibus Dirichlet process mixtures (Q6589247) (← links)
- Estimation of time-specific intervention effects on continuously distributed time-to-event outcomes by targeted maximum likelihood estimation (Q6589248) (← links)
- Conditional transformation models for survivor function estimation (Q6593498) (← links)
- Individual risk prediction: comparing random forests with Cox proportional-hazards model by a simulation study (Q6594174) (← links)
- Identification of representative trees in random forests based on a new tree-based distance measure (Q6613894) (← links)
- Support vector machine for dynamic survival prediction with time-dependent covariates (Q6616360) (← links)
- Genome-wide association study-based deep learning for survival prediction (Q6617408) (← links)
- Tumor heterogeneity estimation for radiomics in cancer (Q6617421) (← links)
- Survival analysis using a 5-step stratified testing and amalgamation routine (5-STAR) in randomized clinical trials (Q6617423) (← links)
- Ranking of average treatment effects with generalized random forests for time-to-event outcomes (Q6617512) (← links)
- deepAFT: a nonlinear accelerated failure time model with artificial neural network (Q6618381) (← links)
- Exploratory subgroup identification in the heterogeneous Cox model: a relatively simple procedure (Q6618404) (← links)
- Weighted least-squares regression with competing risks data (Q6622228) (← links)
- A review on statistical and machine learning competing risks methods (Q6625438) (← links)
- Extreme learning machine Cox model for high-dimensional survival analysis (Q6625631) (← links)
- Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival (Q6625659) (← links)
- Estimating individual treatment effects by gradient boosting trees (Q6627236) (← links)
- Deep learning for survival outcomes (Q6627429) (← links)
- Graphical calibration curves and the integrated calibration index (ICI) for survival models (Q6627540) (← links)
- Estimating heterogeneous survival treatment effect in observational data using machine learning (Q6627965) (← links)
- Uncertainty in lung cancer stage for survival estimation via set-valued classification (Q6628503) (← links)
- Controlled variable selection in Weibull mixture cure models for high-dimensional data (Q6628564) (← links)
- A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data (Q6629877) (← links)
- Marginal structural models with monotonicity constraints: a case study in out-of-hospital cardiac arrest patients (Q6629951) (← links)
- Doubly robust estimation of the hazard difference for competing risks data (Q6629966) (← links)
- Super learner for survival data prediction (Q6636051) (← links)
- Analysis of Large Heterogeneous Repairable System Reliability Data With Static System Attributes and Dynamic Sensor Measurement in Big Data Environment (Q6636542) (← links)
- Regression trees and ensembles for cumulative incidence functions (Q6637103) (← links)
- New forest-based approaches for sufficient dimension reduction (Q6643208) (← links)
- Random survival forests with competing events: a subdistribution-based imputation approach (Q6649350) (← links)
- Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data (Q6652603) (← links)
- A new regression model for the analysis of bimodal censored data: a comparison with random survival forest (Q6654880) (← links)
- Semiparametric copula models applied to the decomposition of claim amounts (Q6656766) (← links)
