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Title: Simulation of synthetic complex data: The R package simPop
Authors : Templ, Matthias
Meindl, Bernhard
Kowarik, Alexander
Dupriez, Olivier
Published in : Journal of Statistical Software
Volume(Issue) : 79
Issue : 10
Pages : 1
Pages to: 38
Publisher / Ed. Institution : UCLA, Dept. of Statistics
Issue Date: 2017
License (according to publishing contract) : CC BY 3.0: Namensnennung 3.0 Unported
Type of review: Peer review (Publication)
Language : English
Subjects : Microdata; Simulation; Synthetic data; Population data; R
Subject (DDC) : 005: Computer programming, programs and data
500: Natural sciences and mathematics
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.
Departement: School of Engineering
Organisational Unit: Institute of Data Analysis and Process Design (IDP)
Publication type: Article in scientific Journal
DOI : 10.18637/jss.v079.i10
ISSN: 1548-7660
Appears in Collections:Publikationen School of Engineering

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