On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time Series Article Swipe
Estimation of the conditional independence graph (CIG) of high-dimensional multivariate Gaussian time series from multi-attribute data is considered. Existing methods for graph estimation for such data are based on single-attribute models where one associates a scalar time series with each node. In multi-attribute graphical models, each node represents a random vector or vector time series. In this paper we provide a unified theoretical analysis of multi-attribute graph learning for dependent time series using a penalized log-likelihood objective function formulated in the frequency domain using the discrete Fourier transform of the time-domain data. We consider both convex (sparse-group lasso) and non-convex (log-sum and SCAD group penalties) penalty/regularization functions. We establish sufficient conditions in a high-dimensional setting for consistency (convergence of the inverse power spectral density to true value in the Frobenius norm), local convexity when using non-convex penalties, and graph recovery. We do not impose any incoherence or irrepresentability condition for our convergence results. We also empirically investigate selection of the tuning parameters based on the Bayesian information criterion, and illustrate our approach using numerical examples utilizing both synthetic and real data.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojsp.2025.3578807
- OA Status
- gold
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411202942
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https://openalex.org/W4411202942Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ojsp.2025.3578807Digital Object Identifier
- Title
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On Conditional Independence Graph Learning From Multi-Attribute Gaussian Dependent Time SeriesWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
J.K. TugnaitList of authors in order
- Landing page
-
https://doi.org/10.1109/ojsp.2025.3578807Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1109/ojsp.2025.3578807Direct OA link when available
- Concepts
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Conditional independence, Independence (probability theory), Series (stratigraphy), Gaussian, Graph, Time series, Computer science, Mathematics, Artificial intelligence, Econometrics, Machine learning, Statistics, Theoretical computer science, Physics, Geology, Quantum mechanics, PaleontologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.domain | 82 |
| abstract_inverted_index.impose | 143 |
| abstract_inverted_index.lasso) | 97 |
| abstract_inverted_index.models | 30 |
| abstract_inverted_index.norm), | 130 |
| abstract_inverted_index.random | 49 |
| abstract_inverted_index.scalar | 35 |
| abstract_inverted_index.series | 12, 37, 71 |
| abstract_inverted_index.tuning | 160 |
| abstract_inverted_index.vector | 50, 52 |
| abstract_inverted_index.Fourier | 86 |
| abstract_inverted_index.density | 123 |
| abstract_inverted_index.inverse | 120 |
| abstract_inverted_index.methods | 19 |
| abstract_inverted_index.models, | 44 |
| abstract_inverted_index.provide | 59 |
| abstract_inverted_index.series. | 54 |
| abstract_inverted_index.setting | 114 |
| abstract_inverted_index.unified | 61 |
| abstract_inverted_index.(log-sum | 100 |
| abstract_inverted_index.Bayesian | 165 |
| abstract_inverted_index.Existing | 18 |
| abstract_inverted_index.Gaussian | 10 |
| abstract_inverted_index.analysis | 63 |
| abstract_inverted_index.approach | 171 |
| abstract_inverted_index.consider | 93 |
| abstract_inverted_index.discrete | 85 |
| abstract_inverted_index.examples | 174 |
| abstract_inverted_index.function | 77 |
| abstract_inverted_index.learning | 67 |
| abstract_inverted_index.results. | 152 |
| abstract_inverted_index.spectral | 122 |
| abstract_inverted_index.Frobenius | 129 |
| abstract_inverted_index.condition | 148 |
| abstract_inverted_index.convexity | 132 |
| abstract_inverted_index.dependent | 69 |
| abstract_inverted_index.establish | 108 |
| abstract_inverted_index.frequency | 81 |
| abstract_inverted_index.graphical | 43 |
| abstract_inverted_index.numerical | 173 |
| abstract_inverted_index.objective | 76 |
| abstract_inverted_index.penalized | 74 |
| abstract_inverted_index.recovery. | 139 |
| abstract_inverted_index.selection | 157 |
| abstract_inverted_index.synthetic | 177 |
| abstract_inverted_index.transform | 87 |
| abstract_inverted_index.utilizing | 175 |
| abstract_inverted_index.Estimation | 0 |
| abstract_inverted_index.associates | 33 |
| abstract_inverted_index.conditions | 110 |
| abstract_inverted_index.criterion, | 167 |
| abstract_inverted_index.estimation | 22 |
| abstract_inverted_index.formulated | 78 |
| abstract_inverted_index.functions. | 106 |
| abstract_inverted_index.illustrate | 169 |
| abstract_inverted_index.non-convex | 99, 135 |
| abstract_inverted_index.parameters | 161 |
| abstract_inverted_index.penalties) | 104 |
| abstract_inverted_index.penalties, | 136 |
| abstract_inverted_index.represents | 47 |
| abstract_inverted_index.sufficient | 109 |
| abstract_inverted_index.conditional | 3 |
| abstract_inverted_index.considered. | 17 |
| abstract_inverted_index.consistency | 116 |
| abstract_inverted_index.convergence | 151 |
| abstract_inverted_index.empirically | 155 |
| abstract_inverted_index.incoherence | 145 |
| abstract_inverted_index.information | 166 |
| abstract_inverted_index.investigate | 156 |
| abstract_inverted_index.theoretical | 62 |
| abstract_inverted_index.time-domain | 90 |
| abstract_inverted_index.(convergence | 117 |
| abstract_inverted_index.independence | 4 |
| abstract_inverted_index.multivariate | 9 |
| abstract_inverted_index.(sparse-group | 96 |
| abstract_inverted_index.log-likelihood | 75 |
| abstract_inverted_index.multi-attribute | 14, 42, 65 |
| abstract_inverted_index.high-dimensional | 8, 113 |
| abstract_inverted_index.single-attribute | 29 |
| abstract_inverted_index.irrepresentability | 147 |
| abstract_inverted_index.penalty/regularization | 105 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5034880586 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| corresponding_institution_ids | https://openalex.org/I82497590 |
| citation_normalized_percentile.value | 0.09527124 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |