A method of sieves for estimating the spectral density

P. Moulin, D. L. Snyder, J. A. O'Sullivan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Maximum-likelihood (ML) spectrum estimation is a notorious illposed problem. In this paper, we are concerned with the use of a regularization method for addressing this fundamental issue. We investigate a method of sieves and present two main results. The first one is a criterion for selecting the mesh size of the sieve, which determines the rate of convergence of the estimates. This criterion is based on information concepts for measuring convergence in the parameter set and is applicable to a wide class of estimation problems. We also recommend a method of sieves based upon a spline representation for the spectral density. The estimates are computationally tractable and consistent in information. The setup of this problem is very general and can be applied without major difficulties to the estimation of higher-dimensional spectral functions.

Original languageEnglish (US)
Title of host publicationProceedings - 1991 IEEE International Symposium on Information Theory, ISIT 1991
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages1
ISBN (Electronic)0780300564
DOIs
StatePublished - Jan 1 1991
Externally publishedYes
Event1991 IEEE International Symposium on Information Theory, ISIT 1991 - Budapest, Hungary
Duration: Jun 24 1991Jun 28 1991

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
ISSN (Print)2157-8095

Conference

Conference1991 IEEE International Symposium on Information Theory, ISIT 1991
CountryHungary
CityBudapest
Period6/24/916/28/91

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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