Electronic Journal of Statistics

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

Debashis Paul and Lili Wang

Full-text: Open access

Abstract

In this discussion, we present a brief overview of recent works on the behavior of summary statistics for high-dimensional observations that are time-dependent, and the inference on parameters associated with high-dimensional time series, with emphasis on covariance and auto-covariance matrices.

Article information

Source
Electron. J. Statist., Volume 10, Number 1 (2016), 74-80.

Dates
Received: March 2015
First available in Project Euclid: 17 February 2016

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1455715956

Digital Object Identifier
doi:10.1214/15-EJS1019

Mathematical Reviews number (MathSciNet)
MR3466176

Zentralblatt MATH identifier
1331.62276

Subjects
Primary: 62H99: None of the above, but in this section

Keywords
Covariance matrix principal component analysis spiked covariance model Stieltjes transform

Citation

Paul, Debashis; Wang, Lili. Discussion of “Estimating structured high-dimensional covariance and precision matrices: Optimal rates and adaptive estimation”. Electron. J. Statist. 10 (2016), no. 1, 74--80. doi:10.1214/15-EJS1019. https://projecteuclid.org/euclid.ejs/1455715956


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References

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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.