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Problems of Information Transmission

, Volume 54, Issue 4, pp 351–371 | Cite as

Noise Level Estimation in High-Dimensional Linear Models

  • G. K. GolubevEmail author
  • E. A. Krymova
Methods of Signal Processing
  • 1 Downloads

Abstract

We consider the problem of estimating the noise level σ2 in a Gaussian linear model Y = +σξ, where ξ ∈ ℝn is a standard discrete white Gaussian noise and β ∈ ℝp an unknown nuisance vector. It is assumed that X is a known ill-conditioned n × p matrix with np and with large dimension p. In this situation the vector β is estimated with the help of spectral regularization of the maximum likelihood estimate, and the noise level estimate is computed with the help of adaptive (i.e., data-driven) normalization of the quadratic prediction error. For this estimate, we compute its concentration rate around the pseudo-estimate ||Y||2/n.

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© Pleiades Publishing, Inc. 2018

Authors and Affiliations

  1. 1.Kharkevich Institute for Information Transmission ProblemsRussian Academy of SciencesMoscowRussia
  2. 2.CNRSAix-Marseille Université, I2MMarseilleFrance
  3. 3.Duisburg-Essen UniversityDuisburgGermany

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