Synthetic Data

Matthias Templ ()
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Matthias Templ: Zurich University of Applied Sciences (ZHAW), Institute of Data Analysis and Process Design (IDP), School of Engineering (SoE)

Chapter Chapter 6 in Statistical Disclosure Control for Microdata, 2017, pp 157-179 from Springer

Abstract: Abstract The generation of synthetic data sets serves as a statistical disclosure control solution to generate public use files out of confidential/protected data. In addition, it is also a tool to create “augmented data sets” which serve as input for micro-simulation models or as data sets for remote execution. 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. In this chapter, the most promising and important method—model-based simulation—is described in detail. It is also the reason why whole populations are simulated rather than only surveys. For other approaches, we refer to Drechsler (2011) (Drechsler, Synthetic data sets for statistical disclosure control. Springer, New York, 2011) and other references below.

Keywords: Random Forest; Synthetic Data; Generalize Pareto Distribution; Primary Sampling Unit; Multinomial Logistic Regression Model (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-50272-4_6

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DOI: 10.1007/978-3-319-50272-4_6

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