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Constructing Graphical Models via the Focused Information Criterion

  • Gerda ClaeskensEmail author
  • Eugen Pircalabelu
  • Lourens Waldorp
Conference paper
Part of the Lecture Notes in Statistics book series (LNS, volume 217)

Abstract

A focused information criterion is developed to estimate undirected graphical models where for each node in the graph a generalized linear model is put forward conditioned upon the other nodes in the graph. The proposed method selects a graph with a small estimated mean squared error for a user-specified focus, which is a function of the parameters in the generalized linear models, by selecting an appropriate model at each node. For situations where the number of nodes is large in comparison with the number of cases, the procedure performs penalized estimation with quadratic approximations to several popular penalties. To show the procedure’s applicability and usefulness we have applied it to two datasets involving voting behavior of U.S. senators and to a clinical dataset on psychopathology.

Keywords

Mean Square Error Exponential Family Vote Behavior Basic Reproduction Number Infected Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The authors wish to thank Prof. J.-H. Kamphuis for the PANAS data. The authors acknowledge the support of the Fund for Scientific Research Flanders, KU Leuven grant GOA/12/14 and of the IAP Research Network P7/06 of the Belgian Science Policy.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Gerda Claeskens
    • 1
    Email author
  • Eugen Pircalabelu
    • 1
  • Lourens Waldorp
    • 2
  1. 1.ORSTAT and Leuven Statistics Research Center, KU LeuvenLeuvenBelgium
  2. 2.Department of Psychological MethodsUniversity of AmsterdamAmsterdamThe Netherlands

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