Synthetic datasets for statistical disclosure control : theory and implementation
The aim of this book is to give the reader a detailed introduction to the different approaches to generating multiply imputed synthetic datasets. It describes all approaches that have been developed so far, provides a brief history of synthetic datasets, and gives useful hints on how to deal with re...
Gardado en:
| Autor Principal: | |
|---|---|
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
New York, NY :
Springer New York
2011.
Cham : Springer Nature |
| Series: | Lecture Notes in Statistics
201 |
| Sujets: | |
| Acceso en liña: | 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: | • Synthetic datasets for statistical disclosure control, theory and implementation, Jörg Drechsler, New York, Springer, 2011, 1 vol. (XX-138 p.), Lecture notes in statistics, 978-1-461-40325-8 • Synthetic Datasets for Statistical Disclosure Control, Texte imprimé, 9781461403272 |
Table des matières:
- Introduction
- Background on Multiply Imputed Synthetic Datasets
- Background on Multiple Imputation
- The IAB Establishment Panel
- Multiple Imputation for Nonresponse
- Fully Synthetic Datasets
- Partially Synthetic Datasets
- Multiple Imputation for Nonresponse and Statistical Disclosure Control
- A Two-Stage Imputation Procedure to Balance the Risk-Utility Trade-Off
- Chances and Obstacles for Multiply Imputed Synthetic Datasets.