Type II chain graph models for categorical data: a smooth subclass

Federica Nicolussi, Roberto Colombi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The Probabilistic Graphical Models use graphs in order to represent the joint distribution of q variables. These models are useful for their ability to capture and represent the system of independence relationships among the variables involved, even when complex. This work concerns categorical variables and the possibility to represent symmetric and asymmetric dependences among categorical variables. For this reason we use the Chain Graphical Models proposed by Andersson, S.A. et al. (2001), also known as Chain Graphical Models of type II (GMs II). The GMs II allow for symmetric relationships typical of log-linear models and, at the same time, asymmetric dependences typical of Graphical Models for Directed Acyclic Graphs. In general, GMs II are not smooth, however this work provides a subclass of smooth GMs II by parameterizing the probability function through marginal log-linear models. Furthermore, the proposed model is applied to a data-set from the European Value Study for the year 2008 EVS (2010).
Original languageEnglish
Pages (from-to)N/A-N/A
Number of pages20
JournalBernoulli
Volume1995-.
DOIs
Publication statusPublished - 2016
Externally publishedYes

Keywords

  • Categorical Variables
  • Chain Graph Models
  • Conditional Indipendence Models
  • Marginal Models

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