Synthetic Datasets for Statistical Disclosure Control: Theory and Implementation by Jörg Drechsler
Synthetic Datasets for Statistical Disclosure Control: Theory and Implementation (Lecture Notes in Statistics) by Jörg Drechsler
English | June 29, 2011 | ISBN: 1461403251 | 159 Pages | PDF | 2 MB
English | June 29, 2011 | ISBN: 1461403251 | 159 Pages | PDF | 2 MB
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 real data problems like nonresponse, skip patterns, or logical constraints.
Each chapter is dedicated to one approach, first describing the general concept followed by a detailed application to a real dataset providing useful guidelines on how to implement the theory in practice.
The discussed multiple imputation approaches include imputation for nonresponse, generating fully synthetic datasets, generating partially synthetic datasets, generating synthetic datasets when the original data is subject to nonresponse, and a two-stage imputation approach that helps to better address the omnipresent trade-off between analytical validity and the risk of disclosure.