publication . Conference object . Preprint . 2016

The goodness of covariance selection problem from AUC bounds

Navid Tafaghodi Khajavi; Anthony Kuh;
Open Access
  • Published: 25 Aug 2016
  • Publisher: IEEE
Abstract
Comment: arXiv admin note: substantial text overlap with arXiv:1605.05776
Persistent Identifiers
Subjects
free text keywords: Computer Science - Information Theory, Toeplitz matrix, Graphical model, Markov chain, Applied mathematics, Model selection, Kullback–Leibler divergence, Covariance, Covariance matrix, Mathematics, Gaussian, symbols.namesake, symbols
Related Organizations
Funded by
NSF| Sensing, Modeling, and Control of Smart Sustainable Microgrids
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1310634
  • Funding stream: Directorate for Engineering | Division of Electrical, Communications & Cyber Systems

[1] A. P. Dempster, “Covariance selection,” Biometrics, vol. 28, no. 1, pp. 157-175, March 1972.

[2] Steffen L Lauritzen, Graphical models, Clarendon Press, 1996.

[3] C. K. Chow and C. N. Liu, “Approximating discrete probability distributions with dependence trees,” IEEE Transactions on Information Theory, pp. 462-467, 1968. [OpenAIRE]

[4] N. T. Khajavi and A. Kuh, “First order markov chain approximation of microgrid renewable generators covariance matrix,” in Proc. of IEEE International Symposium on Information Theory, Istanbul, Turkey (ISIT' 13), July 2013, pp. 1207-1211.

[5] N. Meinshausen and P. Buhlmann, “Model selection through sparse maximum likelihood estimation,” Annals of Statistics, pp. 1436-1464, 2006.

[6] Jerome Friedman, Trevor Hastie, and Robert Tibshirani, “Sparse inverse covariance estimation with the graphical lasso,” Biostatistics, vol. 9, no. 3, pp. 432-441, 2008.

[7] J. B. Kruskal, “On the shortest spanning subtree of a graph and the traveling salesman problem,” Proceedings of the American Mathematical society, vol. 7, no. 1, pp. 48-50, 1956. [OpenAIRE]

Abstract
Comment: arXiv admin note: substantial text overlap with arXiv:1605.05776
Persistent Identifiers
Subjects
free text keywords: Computer Science - Information Theory, Toeplitz matrix, Graphical model, Markov chain, Applied mathematics, Model selection, Kullback–Leibler divergence, Covariance, Covariance matrix, Mathematics, Gaussian, symbols.namesake, symbols
Related Organizations
Funded by
NSF| Sensing, Modeling, and Control of Smart Sustainable Microgrids
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1310634
  • Funding stream: Directorate for Engineering | Division of Electrical, Communications & Cyber Systems

[1] A. P. Dempster, “Covariance selection,” Biometrics, vol. 28, no. 1, pp. 157-175, March 1972.

[2] Steffen L Lauritzen, Graphical models, Clarendon Press, 1996.

[3] C. K. Chow and C. N. Liu, “Approximating discrete probability distributions with dependence trees,” IEEE Transactions on Information Theory, pp. 462-467, 1968. [OpenAIRE]

[4] N. T. Khajavi and A. Kuh, “First order markov chain approximation of microgrid renewable generators covariance matrix,” in Proc. of IEEE International Symposium on Information Theory, Istanbul, Turkey (ISIT' 13), July 2013, pp. 1207-1211.

[5] N. Meinshausen and P. Buhlmann, “Model selection through sparse maximum likelihood estimation,” Annals of Statistics, pp. 1436-1464, 2006.

[6] Jerome Friedman, Trevor Hastie, and Robert Tibshirani, “Sparse inverse covariance estimation with the graphical lasso,” Biostatistics, vol. 9, no. 3, pp. 432-441, 2008.

[7] J. B. Kruskal, “On the shortest spanning subtree of a graph and the traveling salesman problem,” Proceedings of the American Mathematical society, vol. 7, no. 1, pp. 48-50, 1956. [OpenAIRE]

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