Frequentist model averaging for undirected Gaussian graphical models
Liu, Huihang1; Zhang, Xinyu1,2
Source PublicationBIOMETRICS
AbstractAdvances 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.
Keywordgraphical model KL divergence model averaging model uncertainty precision matrix
Indexed BySCI
Funding ProjectChina NNSF Grant[71925007] ; China NNSF Grant[72091212] ; China NNSF Grant[12288201] ; CAS Project for Young Scientists in Basic Research[YSBR-008]
WOS Research AreaLife Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiology ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000862723400001
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Document Type期刊论文
Corresponding AuthorZhang, Xinyu
Affiliation1.Univ Sci & Technol China, Sch Management, Hefei, Anhui, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
Recommended Citation
GB/T 7714
Liu, Huihang,Zhang, Xinyu. Frequentist model averaging for undirected Gaussian graphical models[J]. BIOMETRICS,2022:13.
APA Liu, Huihang,&Zhang, Xinyu.(2022).Frequentist model averaging for undirected Gaussian graphical models.BIOMETRICS,13.
MLA Liu, Huihang,et al."Frequentist model averaging for undirected Gaussian graphical models".BIOMETRICS (2022):13.
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