Semiparametric Theory and Missing Data

1. Introduction to Semiparametric Models

2. Hilbert Space for Random Vectors

3. The Geometry of Influence Functions

4. Semiparametric Models

5. Other Examples of Semiparametric Models

6. Models and Methods for Missing Data

7. Missing and Coarsening at Random for Semiparametric Models

8. The Nuisance Tangent Space and Its Orthogonal Complement

9. Augmented Inverse Probability Weighted Complete-Case Estimators

10. Improving Efficiency and Double Robustness with Coarsened Data

11. Locally Efficient Estimators for Coarsened-Data Semiparametric Models

12. Approximate Methods for Gaining Efficiency

13. Double-Robust Estimator of the Average Causal Treatment Effect

14. Multiple Imputation: A Frequentist Perspective

DRM-restrictions

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Avainsanat: MATHEMATICS / Probability & Statistics / General MAT029000