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https://doi.org/10.21256/zhaw-4237Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Templ, Matthias | - |
| dc.contributor.author | Meindl, Bernhard | - |
| dc.contributor.author | Kowarik, Alexander | - |
| dc.contributor.author | Dupriez, Olivier | - |
| dc.date.accessioned | 2018-05-02T08:56:31Z | - |
| dc.date.available | 2018-05-02T08:56:31Z | - |
| dc.date.issued | 2017 | - |
| dc.identifier.issn | 1548-7660 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/5698 | - |
| dc.description.abstract | The production of synthetic datasets has been proposed as a statistical disclosure control solution to generate public use files out of protected data, and as a tool to create "augmented datasets" to serve as input for micro-simulation models. Synthetic data have become an important instrument for ex-ante assessments of policy impact. The performance and acceptability of such a tool relies heavily on the quality of the synthetic populations, i.e., on the statistical similarity between the synthetic and the true population of interest. Multiple approaches and tools have been developed to generate synthetic data. These approaches can be categorized into three main groups: synthetic reconstruction, combinatorial optimization, and model-based generation. We provide in this paper a brief overview of these approaches, and introduce simPop, an open source data synthesizer. simPop is a user-friendly R package based on a modular object-oriented concept. It provides a highly optimized S4 class implementation of various methods, including calibration by iterative proportional fitting and simulated annealing, and modeling or data fusion by logistic regression. We demonstrate the use of simPop by creating a synthetic population of Austria, and report on the utility of the resulting data. We conclude with suggestions for further development of the package. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | UCLA, Dept. of Statistics | de_CH |
| dc.relation.ispartof | Journal of Statistical Software | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/3.0/ | de_CH |
| dc.subject | Microdata | de_CH |
| dc.subject | Simulation | de_CH |
| dc.subject | Synthetic data | de_CH |
| dc.subject | Population data | de_CH |
| dc.subject | R | de_CH |
| dc.subject.ddc | 005: Computerprogrammierung, Programme und Daten | de_CH |
| dc.subject.ddc | 510: Mathematik | de_CH |
| dc.title | Simulation of synthetic complex data: The R package simPop | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Engineering | de_CH |
| zhaw.organisationalunit | Institut für Datenanalyse und Prozessdesign (IDP) | de_CH |
| dc.identifier.doi | 10.21256/zhaw-4237 | - |
| dc.identifier.doi | 10.18637/jss.v079.i10 | de_CH |
| zhaw.funding.eu | No | de_CH |
| zhaw.issue | 10 | de_CH |
| zhaw.originated.zhaw | No | de_CH |
| zhaw.pages.end | 38 | de_CH |
| zhaw.pages.start | 1 | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 79 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| Appears in collections: | Publikationen School of Engineering | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2017_Templ_Simulation_of_synthetic_complex_data_R_package_simPop.pdf | 1.11 MB | Adobe PDF |  View/Open |
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Templ, M., Meindl, B., Kowarik, A., & Dupriez, O. (2017). Simulation of synthetic complex data: The R package simPop. Journal of Statistical Software, 79(10), 1–38. https://doi.org/10.21256/zhaw-4237
Templ, M. et al. (2017) ‘Simulation of synthetic complex data: The R package simPop’, Journal of Statistical Software, 79(10), pp. 1–38. Available at: https://doi.org/10.21256/zhaw-4237.
M. Templ, B. Meindl, A. Kowarik, and O. Dupriez, “Simulation of synthetic complex data: The R package simPop,” Journal of Statistical Software, vol. 79, no. 10, pp. 1–38, 2017, doi: 10.21256/zhaw-4237.
TEMPL, Matthias, Bernhard MEINDL, Alexander KOWARIK und Olivier DUPRIEZ, 2017. Simulation of synthetic complex data: The R package simPop. Journal of Statistical Software. 2017. Bd. 79, Nr. 10, S. 1–38. DOI 10.21256/zhaw-4237
Templ, Matthias, Bernhard Meindl, Alexander Kowarik, and Olivier Dupriez. 2017. “Simulation of Synthetic Complex Data: The R Package simPop.” Journal of Statistical Software 79 (10): 1–38. https://doi.org/10.21256/zhaw-4237.
Templ, Matthias, et al. “Simulation of Synthetic Complex Data: The R Package simPop.” Journal of Statistical Software, vol. 79, no. 10, 2017, pp. 1–38, https://doi.org/10.21256/zhaw-4237.
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