Constructing Graphical Models via the Focused Information Criterion

Eugen Pircalabelu, Gerda Claeskens and Lourens J. Waldorp
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Eugen Pircalabelu: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2015043, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

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.

Pages: 25
Date: 2015-01-01
Note: In: "Modeling and Stochastic Learning for Forecasting in High Dimensions" - p. 55-78
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2015043

DOI: 10.1007/978-3-319-18732-7

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