Statistically/computationally efficient estimation of non-Gaussian autoregressive processes
Document Type
Conference Proceeding
Date of Original Version
1-1-1987
Abstract
A technique for the estimation of autoregressive filter parameters of a non-Gaussian autoregressive process is proposed. The probability density function of the driving noise is assumed to be known. The technique is a two-stage procedure motivated by maximum likelihood estimation. It is computationally much simpler than the maximum likelihood estimator and does not suffer from convergence problems. Computer simulations indicate that unlike the least squares or linear prediction estimators, the proposed estimator is nearly efficient, even for moderately sized data records.
Publication Title, e.g., Journal
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Citation/Publisher Attribution
Kay, Steven, and Debasis Sengupta. "Statistically/computationally efficient estimation of non-Gaussian autoregressive processes." ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (1987): 45-48. doi: 10.1109/ICASSP.1987.1169857.