An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets
Publication
, Journal Article
Drechsler, J; Reiter, JP
Published in: Computational Statistics and Data Analysis
December 1, 2011
Highlights: Statistical agencies can release simulated data as public use files. Nonparametric regression can be adapted to simulate such datasets. Synthesizers using CART, random forests, support vector machines were compared. CART shown to give highest data utility for acceptable disclosure risk. Nonparametric methods are easy to employ and hence appealing options for agencies. © 2011 Elsevier B.V. All rights reserved.
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Published In
Computational Statistics and Data Analysis
DOI
ISSN
0167-9473
Publication Date
December 1, 2011
Volume
55
Issue
12
Start / End Page
3232 / 3243
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1403 Econometrics
- 0802 Computation Theory and Mathematics
- 0104 Statistics
Citation
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Drechsler, J., & Reiter, J. P. (2011). An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Computational Statistics and Data Analysis, 55(12), 3232–3243. https://doi.org/10.1016/j.csda.2011.06.006
Drechsler, J., and J. P. Reiter. “An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets.” Computational Statistics and Data Analysis 55, no. 12 (December 1, 2011): 3232–43. https://doi.org/10.1016/j.csda.2011.06.006.
Drechsler J, Reiter JP. An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Computational Statistics and Data Analysis. 2011 Dec 1;55(12):3232–43.
Drechsler, J., and J. P. Reiter. “An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets.” Computational Statistics and Data Analysis, vol. 55, no. 12, Dec. 2011, pp. 3232–43. Scopus, doi:10.1016/j.csda.2011.06.006.
Drechsler J, Reiter JP. An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets. Computational Statistics and Data Analysis. 2011 Dec 1;55(12):3232–3243.
Published In
Computational Statistics and Data Analysis
DOI
ISSN
0167-9473
Publication Date
December 1, 2011
Volume
55
Issue
12
Start / End Page
3232 / 3243
Related Subject Headings
- Statistics & Probability
- 4905 Statistics
- 3802 Econometrics
- 1403 Econometrics
- 0802 Computation Theory and Mathematics
- 0104 Statistics