A focused information criterion for graphical models

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

No 2015044, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: A new method for model selection for Gaussian Bayesian networks and Markov networks, with extensions towards ancestral graphs, is constructed to have good mean squared error properties. The method is based on the focused information criterion, and offers the possibility of fitting individual-tailored models. The focus of the research, that is, the purpose of the model, directs the selection. It is shown that using the focused information criterion leads to a graph with small mean squared error. The low mean squared error ensures accurate estimation using a graphical model; here estimation rather than explanation is the main objective. Two situations that commonly occur in practice are treated: a data-driven estimation of a graphical model and the improvement of an already pre-specified feasible model. The search algorithms are illustrated by means of data examples and are compared with existing methods in a simulation study.

Keywords: Theoretical Computer Science; Statistics; Probability and Uncertainty; Statistics and Probability; Computational Theory and Mathematics (search for similar items in EconPapers)
Pages: 26
Date: 2015-01-01
Note: In: Statistics and Computing, vol. 25, no.6, p. 1071-1092 (2015)
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2015044

DOI: 10.1007/s11222-014-9504-y

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