Electronic Journal of Statistics

Discussion of “Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation”

Alexandre B. Tsybakov

Full-text: Open access

Article information

Source
Electron. J. Statist., Volume 10, Number 1 (2016), 67-70.

Dates
Received: January 2016
First available in Project Euclid: 17 February 2016

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1455715954

Digital Object Identifier
doi:10.1214/15-EJS1081A

Mathematical Reviews number (MathSciNet)
MR3466174

Zentralblatt MATH identifier
1331.62278

Citation

Tsybakov, Alexandre B. Discussion of “Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation”. Electron. J. Statist. 10 (2016), no. 1, 67--70. doi:10.1214/15-EJS1081A. https://projecteuclid.org/euclid.ejs/1455715954


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References

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  • [2] I.M. Johnstone (2015), Gaussian estimation: Sequence and wavelet models. Book draft.
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  • [7] K. Lounici (2014) High-dimensional covariance matrix estimation with missing observations., Bernoulli, 20, 2302–2329.
  • [8] A. Rohde and A. B. Tsybakov (2011) Estimation of high-dimensional low rank matrices., Annals of Statistics, 39, 887–930.
  • [9] R. Vershynin (2012) Introduction to the non-asymptotic analysis of random matrices. In:, Compressed Sensing, Theory and Applications. Edited by Y. Eldar and G. Kutyniok, Chapter 5, p. 210–268, Cambridge University Press.

See also

  • Related item: T. Tony Cai, Zhao Ren, Harrison H. Zhou (2016). Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation. Electron. J. Statist. Vol. 10, Iss. 1, 1–59.