JSM 2011 Online Program

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Abstract Details

Activity Number: 242
Type: Contributed
Date/Time: Monday, August 1, 2011 : 2:00 PM to 3:50 PM
Sponsor: Section on Survey Research Methods
Abstract - #302652
Title: An Integrated Adaptive Approach to Data Fusion
Author(s): Hui Xie*+ and YI Qian
Companies: University of Illinois and Northwestern University
Address: , , ,
Keywords: Data Combination ; Nonparametric Method ; MCMC ; Survey ; Multiple Imputation ; Missing Data
Abstract:

Data fusion combines data items from various sources based on a common set of variables. Using the synthesized database, researchers can overcome the limitations of a single-source dataset and answer important questions that cannot be addressed otherwise. We propose an integrated adaptive imputation approach to data fusion method. The proposed method can handle a mixture of continuous, semicontinuous and discrete variables in a robust manner in that no parametric distributional assumption is required for any variable in the data. Therefore the method is applicable to any distributional shapes and can adaptively and automatically generate suitable distributions for any variables to be fused. A simulation study is conducted and shows superior performance of the method as compared with prior approaches. We then apply it to a survey study on counterfeit. The analysis demonstrates that the proposed method can increase the efficiency and validity of data fusion and make data fusion more powerful.


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