Semiparametric Theory and Missing Data
Missing data arise in almost all scientific disciplines. In many cases, the treatment of missing data in an analysis is carried out in a casual and ad-hoc manner, leading, in many cases, to invalid inference and erroneous conclusions. In the past 20 years or so, there has been a serious attempt to u...
| Κύριος συγγραφέας: | |
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| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
| Γλώσσα: | English |
| Έκδοση: |
New York, NY :
Springer New York,
2006.
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| Σειρά: | Springer Series in Statistics,
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| Θέματα: | |
| Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- to Semiparametric Models
- Hilbert Space for Random Vectors
- The Geometry of Influence Functions
- Semiparametric Models
- Other Examples of Semiparametric Models
- Models and Methods for Missing Data
- Missing and Coarsening at Random for Semiparametric Models
- The Nuisance Tangent Space and Its Orthogonal Complement
- Augmented Inverse Probability Weighted Complete-Case Estimators
- Improving Efficiency and Double Robustness with Coarsened Data
- Locally Efficient Estimators for Coarsened-Data Semiparametric Models
- Approximate Methods for Gaining Efficiency
- Double-Robust Estimator of the Average Causal Treatment Effect
- Multiple Imputation: A Frequentist Perspective.
