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M. Templ:
"Simulation of complex synthetic data with the R package simPop";
Keynote Lecture: eRum 2016, Poznan, Polen (invited); 2016-10-12 - 2016-10-14.

The production of synthetic datasets has been proposed as a statistical disclosure con- trol solution to generate public use files out of protected data. This is also a tool to create "augmented datasets" to serve as input for micro-simulation models, and - more generally - the synthetic data sets can be used for design-based simulation studies in general. The performance and acceptability of such a tool relies heavily on the quality of the synthetic data, 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 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, regression tree methods and many other methods.

The production of synthetic datasets has been proposed as a statistical disclosure con- trol solution to generate public use files out of protected data. This is also a tool to create "augmented datasets" to serve as input for micro-simulation models, and - more generally - the synthetic data sets can be used for design-based simulation studies in general. The performance and acceptability of such a tool relies heavily on the quality of the synthetic data, 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 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, regression tree methods and many other methods.

statistical modelling, synthetic data simulation

https://github.com/eRum2016/Book-of-abstracts/raw/master/output/boa.pdf