Classification Through Graphical Models: Evidences From the EU-SILC Data

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.