Asymptotic Analysis of Marginal-Likelihood Based Estimators for m-Dependent Processes
Yair Noam, Joseph Tabrikian · 2006
This paper derives and analyzes the asymptotic performances of the maximum-likelihood (ML) estimator 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 under such a modeling mismatch is based on the marginal likelihood function, and is referred to as marginal maximum likelihood (MML). Under some regularity conditions, the asymptotical distribution of the MML is derived. The asymptotical distributions in some signal processing examples are analyzed. Simulation results support the theory via an example.