000 03846cam a2200553Ki 4500
001 9781315120416
003 FlBoTFG
005 20220509193040.0
006 m o d
007 cr cnu---unuuu
008 190423s2019 flu ob 001 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9781351646727
_q(electronic bk.)
020 _a1351646729
_q(electronic bk.)
020 _a9781315120416
_q(electronic bk.)
020 _a1315120410
_q(electronic bk.)
020 _a9781498727990
_q(electronic bk. : PDF)
020 _a1498727999
_q(electronic bk. : PDF)
020 _a9781351637206
_q(electronic bk. : Mobipocket)
020 _a1351637207
_q(electronic bk. : Mobipocket)
020 _z9781498727983
020 _z1498727980
035 _a(OCoLC)1098174015
035 _a(OCoLC-P)1098174015
050 4 _aQA278.7
_b.A53 2019eb
072 7 _aMAT
_x003000
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072 7 _aMAT
_x029000
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072 7 _aREF
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072 7 _aJMB
_2bicssc
082 0 4 _a519.5/35
_223
245 0 0 _aAnalysis of integrated data /
_cedited by Li-Chun Zhang, Raymond L. Chambers.
264 1 _aBoca Raton, Florida :
_bCRC Press,
_c[2019]
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
520 _aThe advent of "Big Data" has brought with it a rapid diversification of data sources, requiring analysis that accounts for the fact that these data have often been generated and recorded for different reasons. Data integration involves combining data residing in different sources to enable statistical inference, or to generate new statistical data for purposes that cannot be served by each source on its own. This can yield significant gains for scientific as well as commercial investigations. However, valid analysis of such data should allow for the additional uncertainty due to entity ambiguity, whenever it is not possible to state with certainty that the integrated source is the target population of interest. Analysis of Integrated Data aims to provide a solid theoretical basis for this statistical analysis in three generic settings of entity ambiguity: statistical analysis of linked datasets that may contain linkage errors; datasets created by a data fusion process, where joint statistical information is simulated using the information in marginal data from non-overlapping sources; and estimation of target population size when target units are either partially or erroneously covered in each source. Covers a range of topics under an overarching perspective of data integration. Focuses on statistical uncertainty and inference issues arising from entity ambiguity. Features state of the art methods for analysis of integrated data. Identifies the important themes that will define future research and teaching in the statistical analysis of integrated data. Analysis of Integrated Data is aimed primarily at researchers and methodologists interested in statistical methods for data from multiple sources, with a focus on data analysts in the social sciences, and in the public and private sectors.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aMultivariate analysis.
650 0 _aMultiple imputation (Statistics)
650 0 _aMeasurement uncertainty (Statistics)
650 0 _aMathematical statistics.
650 7 _aMATHEMATICS / Applied
_2bisacsh
650 7 _aMATHEMATICS / Probability & Statistics / General
_2bisacsh
650 7 _aREFERENCE / General
_2bisacsh
700 1 _aZhang, Lichun,
_eeditor.
700 1 _aChambers, Raymond L.,
_d1950-
_eeditor.
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781315120416
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c128847
_d128847