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...
Guardado en:
| Autor principal: | |
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| Formato: | Livre numérique |
| Lenguaje: | Anglais |
| Publicado: |
New York, NY :
Springer New York : Springer e-books
[20..].
Cham : Springer Nature |
| Colección: | Springer series in statistics
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| Materias: | |
| Acceso en línea: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Semiparametric theory and missing data, Anastasios A. Tsiatis, New York, Springer, 2006, 1 vol. (XVI-383 p.), Springer series in statistics, 0-38732448-8 |
Tabla de Contenidos:
- 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