The quality of the integration of multiple data sources is key to ensuring that the data can be properly analyzed. This book presents statistical methods for analyzing integrated data, covering uncertainty due to linkage and matching, target population uncertainty, and survey design and analysis. It brings together some of the leading researchers in the field to present the state of the art in methods and applications. Real examples are included throughout for illustration, with software implementation where possible.
Introduction - Ray Chambers
On secondary analysis of datasets that cannot be linked without errors - Li-Chun Zhang
Capture-recapture methods in the presence of linkage errors - Loredana di Congsiglio, Tiziana Tuoto, Li-Chun Zhang
An overview on uncertainty and estimation in statistical matching - Maruo Scanu, Pier Luigi Conti, Daniela Marella
Auxiliary variable selection in a statistical matching problem - Marcello D'Orazio, Marco Di Zio, Mauro Scanu
Minimal inference from incomplete 2 x 2-tables - Li-Chun Zhang, Raymond L. Chambers
Dual and multiple system estimation with fully and partially observed covariates - Van der Heijden et al.
Estimating population size in multiple record systems with uncertainty of state identification - Davide Di Cecco
Log-linear models of erroneous list data - Li-Chun Zhang
Sampling design and analysis using geo-referenced data - Danila Filipponi, Federica Piersimoni, Roberto Benedetti, Maria Michela Dickson, Giuseppe Espa, Diego Giuliani