Double shrinkage estimators for large sparse covariance matrices

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Covariance matrices play an important role in many multivariate techniques and hence a good covariance estimation is crucial in this kind of analysis. In many applications a sparse covariance matrix is expected due to the nature of the data or for simple interpretation. Hard thresholding, soft thresholding, and generalized thresholding were therefore developed to this end. However, these estimators do not always yield well-conditioned covariance estimates. To have sparse and well-conditioned estimates, we propose doubly shrinkage estimators: shrinking small covariances towards zero and then shrinking covariance matrix towards a diagonal matrix. Additionally, a richness index is defined to evaluate how rich a covariance matrix is. According to our simulations, the richness index serves as a good indicator to choose relevant covariance estimator.

Original languageEnglish
Pages (from-to)1497-1511
Number of pages15
JournalJournal of Statistical Computation and Simulation
Issue number8
Publication statusPublished - 2015 May 24

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Double shrinkage estimators for large sparse covariance matrices'. Together they form a unique fingerprint.

Cite this