Abstract
© 2014 Elsevier B.V. All rights reserved. Several attempts to estimate covariance matrices with sparsity constraints have been made. A convex optimization formulation for estimating correlation matrices as opposed to covariance matrices is proposed. An efficient accelerated proximal gradient algorithm is developed, and it is shown that this method gives a faster rate of convergence. An adaptive version of this approach is also discussed. Simulation results and an analysis of a cardiovascular microarray confirm its performance and usefulness.
| Original language | English |
|---|---|
| Pages (from-to) | 390-403 |
| Number of pages | 14 |
| Journal | Computational Statistics and Data Analysis |
| Volume | 93 |
| DOIs | |
| Publication status | Published - 1 Jan 2016 |
| Externally published | Yes |
Keywords
- Accelerated proximal gradient
- Correlation matrix
- High-dimensionality
- Positive definiteness
- Sparsity
ASJC Scopus subject areas
- Statistics and Probability
- Computational Mathematics
- Computational Theory and Mathematics
- Applied Mathematics
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