Indirect Gaussian Graph Learning beyond Gaussianity
This paper studies how to capture dependency graph structures from real data which may not be Gaussian. Starting from marginal loss functions not necessarily derived from probability distributions, we utilize an additive over-parametrization with shrinkage to incorporate variable dependencies into t...
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| Main Authors | , , |
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| Format | Journal Article |
| Language | English |
| Published |
08.10.2016
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| Subjects | |
| Online Access | Get full text |
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| Abstract | This paper studies how to capture dependency graph structures from real data
which may not be Gaussian. Starting from marginal loss functions not
necessarily derived from probability distributions, we utilize an additive
over-parametrization with shrinkage to incorporate variable dependencies into
the criterion. An iterative Gaussian graph learning algorithm is proposed with
ease in implementation. Statistical analysis shows that the estimators achieve
satisfactory accuracy with the error measured in terms of a proper Bregman
divergence. Real-life examples in different settings are given to demonstrate
the efficacy of the proposed methodology. |
|---|---|
| AbstractList | This paper studies how to capture dependency graph structures from real data
which may not be Gaussian. Starting from marginal loss functions not
necessarily derived from probability distributions, we utilize an additive
over-parametrization with shrinkage to incorporate variable dependencies into
the criterion. An iterative Gaussian graph learning algorithm is proposed with
ease in implementation. Statistical analysis shows that the estimators achieve
satisfactory accuracy with the error measured in terms of a proper Bregman
divergence. Real-life examples in different settings are given to demonstrate
the efficacy of the proposed methodology. |
| Author | Tang, Shao Zhang, Qiaoya She, Yiyuan |
| Author_xml | – sequence: 1 givenname: Yiyuan surname: She fullname: She, Yiyuan – sequence: 2 givenname: Shao surname: Tang fullname: Tang, Shao – sequence: 3 givenname: Qiaoya surname: Zhang fullname: Zhang, Qiaoya |
| BackLink | https://doi.org/10.48550/arXiv.1610.02590$$DView paper in arXiv |
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| Snippet | This paper studies how to capture dependency graph structures from real data
which may not be Gaussian. Starting from marginal loss functions not
necessarily... |
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| SubjectTerms | Statistics - Machine Learning Statistics - Methodology |
| Title | Indirect Gaussian Graph Learning beyond Gaussianity |
| URI | https://arxiv.org/abs/1610.02590 |
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