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Analysis of Integrated Data

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advanced statistical inference
Age Group_Uncategorized
Age Group_Uncategorized
automatic-update
B01=Li-Chun Zhang
B01=Raymond L. Chambers
Big Data
Capture Recapture Data
Category1=Non-Fiction
Category=JMA
Category=JMB
Category=KCH
Category=KCHS
Category=PBT
Comparison Data Model
Complex Sample Survey Data
COP=United States
data fusion
data sources
Delivery_Delivery within 10-20 working days
Empirical Error Rates
entity resolution
eq_bestseller
eq_business-finance-law
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_society-politics
Erroneous Enumeration
geo-referenced sampling
Inclusion Probabilities
integrated dataset analysis methods
IPF Algorithm
Language_English
Latent Class Models
Linkage Data Structure
Linkage Errors
List Error
Log Linear Models
Loglinear Models
Matching Variables
Multiple System Estimation
PA=Available
Parametric Bootstrap Confidence Interval
Population Size Estimation
Price_€100 and above
probabilistic data integration
Probabilistic Record Linkage
PS=Active
Record Linkage
Record Linkage Procedure
Secondary Analyst
social science analytics
softlaunch
Spatial Sampling Designs
Standard Log Linear Models
statistical matching
survey sampling
True Match
uncertainty quantification
Vice Versa

Product details

  • ISBN 9781498727983
  • Weight: 552g
  • Dimensions: 156 x 234mm
  • Publication Date: 08 May 2019
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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Description

The 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.
About

Li-Chun Zhang is Professor in Social Statistics at the University of Southampton, UK, Senior Researcher at Statistics Norway, Norway, and Professor in Official Statistics at the University of Oslo, Norway.

Raymond Chambers is Professor of Statistical Methodology at the University of Wollongong, Australia.