On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series Article Swipe
We consider the problem of inferring the conditional independence graph (CIG) of a sparse, high-dimensional stationary multivariate Gaussian time series. A sparse-group lasso-based frequency-domain formulation of the problem based on frequency-domain sufficient statistic for the observed time series is presented. We investigate an alternating direction method of multipliers (ADMM) approach for optimization of the sparse-group lasso penalized log-likelihood. We provide sufficient conditions for convergence in the Frobenius norm of the inverse PSD estimators to the true value, jointly across all frequencies, where the number of frequencies are allowed to increase with sample size. This results also yields a rate of convergence. We also empirically investigate selection of the tuning parameters based on Bayesian information criterion, and illustrate our approach using numerical examples utilizing both synthetic and real data.
Related Topics
Concepts
Lasso (programming language)
Conditional independence
Estimator
Series (stratigraphy)
Algorithm
Mathematics
Rate of convergence
Gaussian
Applied mathematics
Multivariate statistics
Bayesian probability
Computer science
Mathematical optimization
Statistics
Physics
Biology
Paleontology
Channel (broadcasting)
Computer network
World Wide Web
Quantum mechanics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.48550/arxiv.2111.07897
- OA Status
- green
- Cited By
- 1
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4226457926
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4226457926Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2111.07897Digital Object Identifier
- Title
-
On Sparse High-Dimensional Graphical Model Learning For Dependent Time SeriesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-11-15Full publication date if available
- Authors
-
J.K. TugnaitList of authors in order
- Landing page
-
https://doi.org/10.48550/arxiv.2111.07897Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.48550/arxiv.2111.07897Direct OA link when available
- Concepts
-
Lasso (programming language), Conditional independence, Estimator, Series (stratigraphy), Algorithm, Mathematics, Rate of convergence, Gaussian, Applied mathematics, Multivariate statistics, Bayesian probability, Computer science, Mathematical optimization, Statistics, Physics, Biology, Paleontology, Channel (broadcasting), Computer network, World Wide Web, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.inferring | 5 |
| abstract_inverted_index.numerical | 120 |
| abstract_inverted_index.penalized | 56 |
| abstract_inverted_index.selection | 105 |
| abstract_inverted_index.statistic | 32 |
| abstract_inverted_index.synthetic | 124 |
| abstract_inverted_index.utilizing | 122 |
| abstract_inverted_index.conditions | 61 |
| abstract_inverted_index.criterion, | 114 |
| abstract_inverted_index.estimators | 72 |
| abstract_inverted_index.illustrate | 116 |
| abstract_inverted_index.parameters | 109 |
| abstract_inverted_index.presented. | 39 |
| abstract_inverted_index.stationary | 15 |
| abstract_inverted_index.sufficient | 31, 60 |
| abstract_inverted_index.alternating | 43 |
| abstract_inverted_index.conditional | 7 |
| abstract_inverted_index.convergence | 63 |
| abstract_inverted_index.empirically | 103 |
| abstract_inverted_index.formulation | 24 |
| abstract_inverted_index.frequencies | 85 |
| abstract_inverted_index.information | 113 |
| abstract_inverted_index.investigate | 41, 104 |
| abstract_inverted_index.lasso-based | 22 |
| abstract_inverted_index.multipliers | 47 |
| abstract_inverted_index.convergence. | 100 |
| abstract_inverted_index.frequencies, | 80 |
| abstract_inverted_index.independence | 8 |
| abstract_inverted_index.multivariate | 16 |
| abstract_inverted_index.optimization | 51 |
| abstract_inverted_index.sparse-group | 21, 54 |
| abstract_inverted_index.log-likelihood. | 57 |
| abstract_inverted_index.frequency-domain | 23, 30 |
| abstract_inverted_index.high-dimensional | 14 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| 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 | 0.50440154 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |