On the sparsity of signals in a random sample

• 成果类型: Article
• 署名作者: Jiang, Binyan; Loh, Wei-Liem
• 署名单位: National University of Singapore
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/ass039
• 发表日期: 2012
• 页码: 915928
• 关键词: Deconvolution; rates; CONVERGENCE

摘要

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.