On the sparsity of signals in a random sample

Binyan Jiang, Wei Liem Loh

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

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 languageEnglish
Pages (from-to)915-928
Number of pages14
JournalBiometrika
Volume99
Issue number4
DOIs
Publication statusPublished - 1 Dec 2012
Externally publishedYes

Keywords

  • Large covariance matrix
  • Method of moments
  • Signal sequence
  • Sparsity
  • Trigonometric matrix

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics(all)
  • Agricultural and Biological Sciences (miscellaneous)
  • Agricultural and Biological Sciences(all)
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

Cite this