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Abstract Details

Activity Number: 440
Type: Invited
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Survey Research Methods
Abstract - #303631
Title: Data Fusion via Multiple Imputation
Author(s): Jerome P Reiter*+
Companies: Duke University
Address: Box 90251, Durham, NC, , USA
Keywords: matching ; missing ; Bayesian ; imputation ; fusion
Abstract:

Often analysts seek to combine information from two (or more) different sources, i.e., data fusion. I discuss a framework for multiple imputation inferences for data fusion contexts in which two sources do not have any overlapping records. The basic idea is to (i) formulate a joint model for the concatenated data, (ii) posit values of any unobservable parameters in the model, and (iii) generate multiple imputations under the posited model. However, in this context, the usual multiple imputation combining rules of Rubin (1987) lead to biased estimates of variance, even when the posited parameter values are correct. I present an alternative framework that enables valid estimation of variances for these data fusion settings.


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