Multiple Imputation and Random Forests (MIRF) for Unobservable, High-Dimensional Data

Articles in the same Issue

1. Article
2. Conditional Likelihood Inference in a Case-Cohort Design: An Application to Haplotype Analysis
3. A Resampling-Based Approach to Multiple Testing with Uncertainty in Phase
4. Causal Effect Models for Realistic Individualized Treatment and Intention to Treat Rules
5. A Doubly Robust Censoring Unbiased Transformation
6. Regression Analysis of Mean Lifetime: Exploring Nonlinear Relationship with Heteroscedasticity
7. Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules
8. Survival Instantaneous Log-odds Ratio from Empirical Functions
9. The Analysis of Bivariate Truncated Data Using the Clayton Copula Model
10. Phase Variation in Child and Adolescent Growth
11. An Integrated Analysis of Individual and Aggregated Health Data Using Estimating Equations
12. The Benjamini-Hochberg Method in the Case of Discrete Test Statistics
13. Multiple Imputation and Random Forests (MIRF) for Unobservable, High-Dimensional Data
14. Modeling the Effect of Alzheimer's Disease on Mortality
15. Preference-Based Instrumental Variable Methods for the Estimation of Treatment Effects: Assessing Validity and Interpreting Results