A double regression method for graphical modeling of high-dimensional nonlinear and non-Gaussian data Article Swipe
Graphical models have long been studied in statistics as a tool for inferring conditional independence relationships among a large set of random variables. The most existing works in graphical modeling focus on the cases that the data are Gaussian or mixed and the variables are linearly dependent. In this paper, we propose a double regression method for learning graphical models under the high-dimensional nonlinear and non-Gaussian setting, and prove that the proposed method is consistent under mild conditions. The proposed method works by performing a series of nonparametric conditional independence tests. The conditioning set of each test is reduced via a double regression procedure where a model-free sure independence screening procedure or a sparse deep neural network can be employed. The numerical results indicate that the proposed method works well for high-dimensional nonlinear and non-Gaussian data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.4310/22-sii756
- OA Status
- green
- Cited By
- 2
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4400812588
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Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4400812588Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.4310/22-sii756Digital Object Identifier
- Title
-
A double regression method for graphical modeling of high-dimensional nonlinear and non-Gaussian dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-01Full publication date if available
- Authors
-
Siqi Liang, Faming LiangList of authors in order
- Landing page
-
https://doi.org/10.4310/22-sii756Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.ncbi.nlm.nih.gov/pmc/articles/11756891Direct OA link when available
- Concepts
-
Mathematics, Statistics, Regression, Graphical model, Nonlinear regression, High dimensional, Gaussian, Regression analysis, Computer science, Data mining, Pattern recognition (psychology), Econometrics, Artificial intelligence, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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