Title :
Parameter estimation for non-Gaussian autoregressive processes
Author :
Beadle, Edward R. ; Djuric, Petar M.
Author_Institution :
Dept. of Electr. Eng., State Univ. of New York, Stony Brook, NY, USA
Abstract :
It is proposed to jointly estimate the parameters of non-Gaussian autoregressive (AR) processes in a Bayesian context using the Gibbs sampler. Using the Markov chains produced by the sampler an approximation to the vector MAP estimator is implemented. The results reported here used AR(4) models driven by noise sequences where each sample is i.i.d. as a two component Gaussian sum mixture. The results indicate that using the Gibbs sampler to approximate the vector MAP estimator provides estimates with precision that compares favorably with the CRLBs. Also discussed are issues regarding the implementation of the Gibbs sampler for AR mixture models
Keywords :
Bayes methods; Gaussian processes; Markov processes; maximum likelihood estimation; noise; sequences; signal sampling; statistical analysis; AR mixture models; Bayesian method; CRLB; Gibbs sampler; Markov chains; i.i.d. sample; noise sequences; nonGaussian autoregressive processes; parameter estimation; statistical signal analysis; two component Gaussian sum mixture; vector MAP estimator approximation; Autoregressive processes; Bayesian methods; Gaussian noise; Gaussian processes; Least squares approximation; Least squares methods; Parameter estimation; Signal analysis; State estimation; Transfer functions;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Conference_Location :
Munich
Print_ISBN :
0-8186-7919-0
DOI :
10.1109/ICASSP.1997.604634
