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A focused information criterion for graphical models

Author

Listed:
  • Pircalabelu, Eugen

    (Université catholique de Louvain, LIDAM/ISBA, Belgium)

  • Claeskens, Gerda
  • Waldorp, Lourens J.

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.

Suggested Citation

  • Pircalabelu, Eugen & Claeskens, Gerda & Waldorp, Lourens J., 2015. "A focused information criterion for graphical models," LIDAM Reprints ISBA 2015044, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2015044
    DOI: https://doi.org/10.1007/s11222-014-9504-y
    Note: In: Statistics and Computing, vol. 25, no.6, p. 1071-1092 (2015)
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    Cited by:

    1. Pircalabelu, Eugen & Claeskens, Gerda, 2021. "Linear manifold modeling and graph estimation based on multivariate functional data with different coarseness scales," LIDAM Discussion Papers ISBA 2021032, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. S. C. Pandhare & T. V. Ramanathan, 2020. "The focussed information criterion for generalised linear regression models for time series," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(4), pages 485-507, December.
    3. Wei, Yuting & Wang, Qihua & Duan, Xiaogang & Qin, Jing, 2021. "Bias-corrected Kullback–Leibler distance criterion based model selection with covariables missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 160(C).
    4. Jiang Du & Zhongzhan Zhang & Tianfa Xie, 2017. "Focused information criterion and model averaging in censored quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 80(5), pages 547-570, July.

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