Statistical matching using fractional imputation
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by Jae Kwang Kim, Emily Berg and Taesung ParkNote 1
- Release date: June 22, 2016
Statistical matching is a technique for integrating two or more data sets when information available for matching records for individual participants across data sets is incomplete. Statistical matching can be viewed as a missing data problem where a researcher wants to perform a joint analysis of variables that are never jointly observed. A conditional independence assumption is often used to create imputed data for statistical matching. We consider a general approach to statistical matching using parametric fractional imputation of Kim (2011) to create imputed data under the assumption that the specified model is fully identified. The proposed method does not have a convergent expectation-maximisation (EM) sequence if the model is not identified. We also present variance estimators appropriate for the imputation procedure. We explain how the method applies directly to the analysis of data from split questionnaire designs and measurement error models.Key Words: Data combination; Data fusion; Hot deck imputation; Split questionnaire design; Measurement error model.
Table of content
- 1. Introduction
- 2. Basic setup
- 3. Fractional imputation
- 4. Split questionnaire survey design
- 5. Measurement error models
- 6. Simulation study
- 7. Concluding remarks