The following pages link to SuperLearner (Q20166):
Displaying 38 items.
- When and when not to use optimal model averaging (Q2208423) (← links)
- Predicting Rice phenotypes with meta and multi-target learning (Q2217409) (← links)
- Comment: Outcome-wide individualized treatment strategies (Q2218079) (← links)
- Rejoinder: A nonparametric superefficient estimator of the average treatment effect (Q2218089) (← links)
- A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics (Q2259821) (← links)
- Improved precision in the analysis of randomized trials with survival outcomes, without assuming proportional hazards (Q2274689) (← links)
- Robust and flexible estimation of stochastic mediation effects: a proposed method and example in a randomized trial setting (Q2324996) (← links)
- Comment on ``Automated versus do-it-yourself methods for causal inference: lessons learned from a data analysis competition'' (Q2325614) (← links)
- Efficient estimation of quantiles in missing data models (Q2409634) (← links)
- Flexible HAR model for realized volatility (Q2697034) (← links)
- Identification and efficient estimation of the natural direct effect among the untreated (Q2846436) (← links)
- Global sensitivity analysis for repeated measures studies with informative drop-out: A semi-parametric approach (Q3119826) (← links)
- An Omnibus Non-Parametric Test of Equality in Distribution for Unknown Functions (Q3120101) (← links)
- Tuning parameters in random forests (Q4606433) (← links)
- Impact of subsampling and tree depth on random forests (Q4615432) (← links)
- Confidence sets with expected sizes for Multiclass Classification (Q4637018) (← links)
- An Application of Targeted Maximum Likelihood Estimation to the Meta‐Analysis of Safety Data (Q4919598) (← links)
- Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods (Q5034151) (← links)
- The relative performance of ensemble methods with deep convolutional neural networks for image classification (Q5036355) (← links)
- Mortality forecasting using stacked regression ensembles (Q5042782) (← links)
- (Q5053280) (← links)
- Comparing different propensity score estimation methods for estimating the marginal causal effect through standardization to propensity scores (Q5084764) (← links)
- Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification (Q5093035) (← links)
- Comparing and Weighting Imperfect Models Using D-Probabilities (Q5120673) (← links)
- Hands-On Machine Learning with R (Q5206307) (← links)
- Aggregating classifiers via Rademacher–Walsh polynomials (Q5220783) (← links)
- Discussion of PENCOMP (Q5229884) (← links)
- Nonparametric Causal Effects Based on Incremental Propensity Score Interventions (Q5231493) (← links)
- Super Learner (Q5443060) (← links)
- Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes (Q5857116) (← links)
- Optimal Individualized Decision Rules Using Instrumental Variable Methods (Q5857139) (← links)
- Robust Q-Learning (Q5857152) (← links)
- Comparing the performance of statistical methods that generalize effect estimates from randomized controlled trials to much larger target populations (Q5867468) (← links)
- Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive Models (Q5881076) (← links)
- Sensitivity Analysis via the Proportion of Unmeasured Confounding (Q5881155) (← links)
- Nonparametric Tests of the Causal Null With Nondiscrete Exposures (Q5881156) (← links)
- Discussion of: Treelets -- an adaptive multi-scale basis for sparse unordered data (Q5970956) (← links)
- tidyhte (Q5983158) (← links)
