The Annals of Statistics

Discussion: Latent variable graphical model selection via convex optimization

Christophe Giraud and Alexandre Tsybakov

Full-text: Open access

Article information

Source
Ann. Statist., Volume 40, Number 4 (2012), 1984-1988.

Dates
First available in Project Euclid: 30 October 2012

Permanent link to this document
https://projecteuclid.org/euclid.aos/1351602531

Digital Object Identifier
doi:10.1214/12-AOS984

Mathematical Reviews number (MathSciNet)
MR3059071

Zentralblatt MATH identifier
1288.62087

Citation

Giraud, Christophe; Tsybakov, Alexandre. Discussion: Latent variable graphical model selection via convex optimization. Ann. Statist. 40 (2012), no. 4, 1984--1988. doi:10.1214/12-AOS984. https://projecteuclid.org/euclid.aos/1351602531


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References

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  • [3] Giraud, C. (2008). Estimation of Gaussian graphs by model selection. Electron. J. Stat. 2 542–563.
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  • [7] Ravikumar, P., Wainwright, M. J., Raskutti, G. and Yu, B. (2011). High-dimensional covariance estimation by minimizing $\ell_1$-penalized log-determinant divergence. Electron. J. Stat. 5 935–980.
  • [8] Sun, T. and Zhang, C. H. (2012). Sparse matrix inversion with scaled lasso. Available at arXiv:1202.2723.
  • [9] Villers, F., Schaeffer, B., Bertin, C. and Huet, S. (2008). Assessing the validity domains of graphical Gaussian models in order to infer relationships among components of complex biological systems. Stat. Appl. Genet. Mol. Biol. 7 Art. 14, 36.

See also

  • Main article: Latent variable graphical model selection via convex optimization.