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Frequentist model averaging for undirected Gaussian graphical models

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  • Huihang Liu
  • Xinyu Zhang

Abstract

Advances in information technologies have made network data increasingly frequent in a spectrum of big data applications, which is often explored by probabilistic graphical models. To precisely estimate the precision matrix, we propose an optimal model averaging estimator for Gaussian graphs. We prove that the proposed estimator is asymptotically optimal when candidate models are misspecified. The consistency and the asymptotic distribution of model averaging estimator, and the weight convergence are also studied when at least one correct model is included in the candidate set. Furthermore, numerical simulations and a real data analysis on yeast genetic data are conducted to illustrate that the proposed method is promising.

Suggested Citation

  • Huihang Liu & Xinyu Zhang, 2023. "Frequentist model averaging for undirected Gaussian graphical models," Biometrics, The International Biometric Society, vol. 79(3), pages 2050-2062, September.
    • Handle: RePEc:bla:biomet:v:79:y:2023:i:3:p:2050-2062
    DOI: 10.1111/biom.13758
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    References listed on IDEAS

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