Generalizing analytic shrinkage for arbitrary covariance structures

Daniel Bartz, Klaus Robert Müller

Research output: Contribution to journalConference articlepeer-review

12 Citations (Scopus)


Analytic shrinkage is a statistical technique that offers a fast alternative to crossvalidation for the regularization of covariance matrices and has appealing consistency properties. We show that the proof of consistency requires bounds on the growth rates of eigenvalues and their dispersion, which are often violated in data. We prove consistency under assumptions which do not restrict the covariance structure and therefore better match real world data. In addition, we propose an extension of analytic shrinkage-orthogonal complement shrinkage-which adapts to the covariance structure. Finally we demonstrate the superior performance of our novel approach on data from the domains of finance, spoken letter and optical character recognition, and neuroscience.

Original languageEnglish
JournalAdvances in Neural Information Processing Systems
Publication statusPublished - 2013
Event27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States
Duration: 2013 Dec 52013 Dec 10

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing


Dive into the research topics of 'Generalizing analytic shrinkage for arbitrary covariance structures'. Together they form a unique fingerprint.

Cite this