Abstract
This article proposes a method of moments technique for estimating the sparsity of signals in a random sample. This involves estimating the largest eigenvalue of a large Hermitian trigonometric matrix under mild conditions. As illustration, the method is applied to two well-known problems. The first focuses on the sparsity of a large covariance matrix and the second investigates the sparsity of a sequence of signals observed with stationary, weakly dependent noise. Simulation shows that the proposed estimators can have significantly smaller mean absolute errors than their main competitors.
Original language | English |
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Pages (from-to) | 915-928 |
Number of pages | 14 |
Journal | Biometrika |
Volume | 99 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Dec 2012 |
Externally published | Yes |
Keywords
- Large covariance matrix
- Method of moments
- Signal sequence
- Sparsity
- Trigonometric matrix
ASJC Scopus subject areas
- Statistics and Probability
- General Mathematics
- Agricultural and Biological Sciences (miscellaneous)
- General Agricultural and Biological Sciences
- Statistics, Probability and Uncertainty
- Applied Mathematics