Marginal likelihood for estimation and detection theory

Yair Noam, Joseph Tabrikian

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


This paper derives and analyzes the asymptotic performances of the maximum-likelihood (ML) estimator and the generalized likelihood ratio test (GLRT) derived under the assumption of independent identically distribution (i.i.d.) samples, where in the actual model the signal samples are m-dependent. The ML and GLRT under such a modeling mismatch are based on the marginal likelihood function, and they are referred to as marginal maximum likelihood (MML) and "generalized (sum) marginal log-likelihood ratio test"(GMLRT), respectively. Under some regularity conditions, the asymptotical distributions of the MML and GMLRT are derived. The asymptotical distributions in some signal processing examples are analyzed. Simulation results support the theory via several examples.

Original languageEnglish
Pages (from-to)3963-3974
Number of pages12
JournalIEEE Transactions on Signal Processing
Issue number8
StatePublished - 1 Aug 2007


  • Asymptotic properties
  • Consistency
  • Detection
  • Estimation
  • Generalized (sum) marginal log-likelihood ratio test (GMLRT)
  • Marginal likelihood
  • Marginal maximum likelihood (MML)
  • m-dependent

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


Dive into the research topics of 'Marginal likelihood for estimation and detection theory'. Together they form a unique fingerprint.

Cite this