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Cubierta del libro
Autor Drechsler, Jörg. author.

Título Synthetic Datasets for Statistical Disclosure Control [electronic resource] : Theory and Implementation / by Jörg Drechsler.

Publicación New York, NY : Springer New York, 2011.
Edición 1.
Descripción física XX, 138 p. 19 illus. online resource.
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Colección Lecture Notes in Statistics, 0930-0325 ; 201
Lecture Notes in Statistics, 0930-0325 ; 201
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Contiene: 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.
Resumen: 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. The book concludes with a glimpse into the future of synthetic datasets, discussing the potential benefits and possible obstacles of the approach and ways to address the concerns of data users and their understandable discomfort with using data that doesń鴠consist only of the originally collected values.  The book is intended for researchers and practitioners alike. It helps the researcher to find the state of the art in synthetic data summarized in one book with full reference to all relevant papers on the topic. But it is also useful for the practitioner at the statistical agency who is considering the synthetic data approach for data dissemination in the future and wants to get familiar with the topic.
Materia Statistics.
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Statistics for Business/Economics/Mathematical Finance/Insurance.
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Statistics for Life Sciences, Medicine, Health Sciences.
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Autor secundario SpringerLink (Online service)
En Springer eBooks
OTRO SOPORTE Printed edition: 9781461403258
ISBN 9781461403265 978-1-4614-0326-5
ISBN/ISSN 10.1007/978-1-4614-0326-5 doi