Frontmatter

Artikel in diesem Heft

1. Frontmatter
2. Editorial
3. Special Issue on Data-Adaptive Statistical Inference
4. Research Articles
5. Statistical Inference for Data Adaptive Target Parameters
6. Evaluations of the Optimal Discovery Procedure for Multiple Testing
7. Addressing Confounding in Predictive Models with an Application to Neuroimaging
8. Model-Based Recursive Partitioning for Subgroup Analyses
9. The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data
10. A Sequential Rejection Testing Method for High-Dimensional Regression with Correlated Variables
11. Variable Selection for Confounder Control, Flexible Modeling and Collaborative Targeted Minimum Loss-Based Estimation in Causal Inference
12. Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences
13. A Case Study of the Impact of Data-Adaptive Versus Model-Based Estimation of the Propensity Scores on Causal Inferences from Three Inverse Probability Weighting Estimators
14. Influence Re-weighted G-Estimation
15. Optimal Spatial Prediction Using Ensemble Machine Learning
16. AUC-Maximizing Ensembles through Metalearning
17. Selection Bias When Using Instrumental Variable Methods to Compare Two Treatments But More Than Two Treatments Are Available
18. Doubly Robust and Efficient Estimation of Marginal Structural Models for the Hazard Function
19. Data-Adaptive Bias-Reduced Doubly Robust Estimation
20. Optimal Individualized Treatments in Resource-Limited Settings
21. Super-Learning of an Optimal Dynamic Treatment Rule
22. Second-Order Inference for the Mean of a Variable Missing at Random
23. One-Step Targeted Minimum Loss-based Estimation Based on Universal Least Favorable One-Dimensional Submodels