| 書目名稱 | Synthetic Datasets for Statistical Disclosure Control | | 副標題 | Theory and Implement | | 編輯 | J?rg Drechsler | | 視頻video | http://file.papertrans.cn/885/884357/884357.mp4 | | 概述 | Is the first book that fully covers all different approaches to generating multiply imputed synthetic datasets.Combination of theory and practical implementation issues makes it appealing to the resea | | 叢書名稱 | Lecture Notes in Statistics | | 圖書封面 |  | | 描述 | .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 wit | | 出版日期 | Book 2011 | | 關(guān)鍵詞 | confidentiality; disclosure; multiple imputation; synthetic | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4614-0326-5 | | isbn_softcover | 978-1-4614-0325-8 | | isbn_ebook | 978-1-4614-0326-5Series ISSN 0930-0325 Series E-ISSN 2197-7186 | | issn_series | 0930-0325 | | copyright | Springer Science+Business Media, LLC 2011 |
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