TY - GEN
T1 - Asymptotic analysis of marginal-likelihood based estimators for m-dependent processes
AU - Noam, Yair
AU - Tabrikian, Joseph
PY - 2006/12/1
Y1 - 2006/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=50249092845&partnerID=8YFLogxK
U2 - 10.1109/EEEI.2006.321070
DO - 10.1109/EEEI.2006.321070
M3 - Conference contribution
AN - SCOPUS:50249092845
SN - 1424402301
SN - 9781424402304
T3 - IEEE Convention of Electrical and Electronics Engineers in Israel, Proceedings
SP - 275
EP - 279
BT - 2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI
T2 - 2006 IEEE 24th Convention of Electrical and Electronics Engineers in Israel, IEEEI
Y2 - 15 November 2006 through 17 November 2006
ER -