This paper derives and analyzes the asymptotic performances of the maximumlikelihood (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 mdependent. 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 loglikelihood 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.
Date of Award  2007 

Original language  American English 

Awarding Institution   BenGurion University of the Negev


Supervisor  Joseph Tabrikian (Supervisor) 

Marginal likelihood for estimation and detection theory
Noam, Y. (Author), Joseph Tabrikian (Author). 2007
Student thesis: Thesis