Bayesian finite population imputation for data fusion
Publication
, Journal Article
Reiter, JP
Published in: Statistica Sinica
April 1, 2012
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.
Duke Scholars
Published In
Statistica Sinica
DOI
ISSN
1017-0405
Publication Date
April 1, 2012
Volume
22
Issue
2
Start / End Page
795 / 811
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics
Citation
APA
Chicago
ICMJE
MLA
NLM
Reiter, J. P. (2012). Bayesian finite population imputation for data fusion. Statistica Sinica, 22(2), 795–811. https://doi.org/10.5705/ss.2010.140
Reiter, J. P. “Bayesian finite population imputation for data fusion.” Statistica Sinica 22, no. 2 (April 1, 2012): 795–811. https://doi.org/10.5705/ss.2010.140.
Reiter JP. Bayesian finite population imputation for data fusion. Statistica Sinica. 2012 Apr 1;22(2):795–811.
Reiter, J. P. “Bayesian finite population imputation for data fusion.” Statistica Sinica, vol. 22, no. 2, Apr. 2012, pp. 795–811. Scopus, doi:10.5705/ss.2010.140.
Reiter JP. Bayesian finite population imputation for data fusion. Statistica Sinica. 2012 Apr 1;22(2):795–811.
Published In
Statistica Sinica
DOI
ISSN
1017-0405
Publication Date
April 1, 2012
Volume
22
Issue
2
Start / End Page
795 / 811
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 0801 Artificial Intelligence and Image Processing
- 0199 Other Mathematical Sciences
- 0104 Statistics