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\footnotesize \noindent Statistica Sinica {\bf 22} (2012), 795-811

\footnotesize \noindent doi:http://dx.doi.org/10.5705/ss.2010.140

\vspace{3pc} \normalsize \centerline{\large\bf BAYESIAN FINITE
POPULATION  IMPUTATION} \vspace{2pt} \centerline{\large\bf  FOR DATA
FUSION} \vspace{.25cm}
\author{Jerome P. Reiter}
\vspace{.4cm} \centerline{\it Duke University} \vspace{.55cm} 
\begin{quotation}
\noindent {\it Abstract:} In data fusion, data owners seek to
combine datasets with disjoint observations and distinct variables
to estimate relationships among the variables.   One approach is to
concatenate the files, specify models relating the variables not
jointly observed, and use the models to generate multiple
imputations of the missing  data. We show that the standard multiple
imputation estimator of the sampling variance can have positive bias
in such contexts.  We present an approach for correcting this
problem based on Bayesian finite population inference. We also
present an approach for data fusion when some values are
confidential and cannot be shared.\par \vspace{9pt} \noindent {\it
Key words and phrases:} Confidentiality, disclosure, matching,
multiple, sharing, synthetic. \par
\end{quotation}

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