Context-Specific independencies embedded in Chain Graph Models of type I

For a set of variables collected in a contingency table, we focus on a particular kind of relationships such as the context-specific independencies. These are conditional independencies that hold for particular values of the conditioning set. Given the advantages of the graphical models, we use them to represent different relationships among the variables, including the context-specific independencies. In particular, we enrich chain graph models with labelled arcs. Furthermore, we consider the well-known relationships between chain graph models and hierarchical multinomial marginal models and we introduce new constraints on parameters in order to describe the context-specific relationship. Finally, we provide an application to the study of innovation in Italy by comparing two different periods.