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

Volume

5

fYear

1997

fDate

21-24 Apr 1997

Firstpage

3557

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on

Conference_Location

Munich

ISSN

1520-6149

Print_ISBN

0-8186-7919-0

Type

conf

DOI

10.1109/ICASSP.1997.604634

Filename

604634