000			03821nam a22004695i 4500
001			978-1-4614-0326-5
003			DE-He213
005			20140220083732.0
007			cr nn 008mamaa
008			110623s2011 xxu| s |||| 0|eng d
020			_a9781461403265 _9978-1-4614-0326-5
024	7		_a10.1007/978-1-4614-0326-5 _2doi
050		4	_aQA276-280
072		7	_aJHBC _2bicssc
072		7	_aSOC027000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aDrechsler, Jörg. _eauthor.
245	1	0	_aSynthetic Datasets for Statistical Disclosure Control _h[electronic resource] : _bTheory and Implementation / _cby Jörg Drechsler.
250			_a1.
264		1	_aNew York, NY : _bSpringer New York, _c2011.
300			_aXX, 138p. 19 illus. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aLecture Notes in Statistics, _x0930-0325 ; _v201
505	0		_aIntroduction -- 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.
520			_aThe 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 doesn’t 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.
650		0	_aStatistics.
650		0	_aEconomics _xStatistics.
650	1	4	_aStatistics.
650	2	4	_aStatistics for Social Science, Behavorial Science, Education, Public Policy, and Law.
650	2	4	_aStatistics for Business/Economics/Mathematical Finance/Insurance.
650	2	4	_aStatistics for Life Sciences, Medicine, Health Sciences.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781461403258
830		0	_aLecture Notes in Statistics, _x0930-0325 ; _v201
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4614-0326-5
912			_aZDB-2-SMA
999			_c106236 _d106236