Title :
Inversion of H-ARMA models
Author :
Declercq, David ; Duvaut, Patrick ; Soubielle, Jerome
Author_Institution :
ETIS, URA, Cergy-Pontoise, France
Abstract :
We present in this contribution the problem of nongaussian H-ARMA models inversion. We show that very-classical methods of parameters identification based on the likelihood are unefficients in our case and we have chosen a fractionnal distance minimisation approach to estimate the nonlinearity. The ARMA coefficients are identified with maximum likelihood estimators and a comparison study with the cumulant based method has been conducted on synthetic data.
Keywords :
autoregressive moving average processes; maximum likelihood estimation; minimisation; parameter estimation; ARMA coefficients; fractionnal distance minimisation approach; maximum likelihood estimators; nongaussian H-ARMA models inversion; nonlinearity; parameters identification; Bayes methods; Computational modeling; Gaussian noise; Mathematical model; Maximum likelihood estimation; Polynomials;
Conference_Titel :
Signal Processing Conference (EUSIPCO 1998), 9th European
Conference_Location :
Rhodes
Print_ISBN :
978-960-7620-06-4
