Abstract
The purpose of this work is to evaluate the level of perceived health by studying possible factors such as personal information, economic status, and use of free time. The analysis is carried out on the European Union Statistics on Income and Living Conditions (EU-SILC) survey covering 31 European countries. At this aim, we take advantage of graphical models that are suitable tools to represent complex dependence structures among a set of variables. In particular, we consider a special case of Chain Graph model, known as Chain Graph models of type IV for categorical variables. We implement a Bayesian learning procedure to discover the graph which best represents the dataset. Finally, we perform a classification algorithm based on classification trees to identify clusters.
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Acknowledgements
This paper is based on data from Eurostat, EU Statistics on Income and Living Conditions [2016].
The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 730998, InGRID-2 Integrating Research Infrastructure for European expertise on Inclusive Growth from data to policy.
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Nicolussi, F., Di Brisco, A.M., Cazzaro, M. (2021). Classification Through Graphical Models: Evidences From the EU-SILC Data. In: Chadjipadelis, T., Lausen, B., Markos, A., Lee, T.R., Montanari, A., Nugent, R. (eds) Data Analysis and Rationality in a Complex World. IFCS 2019. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-030-60104-1_22
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