Simple and Efficient Estimation of Parameters of Non-Gaussian Autoregressive Processes
Abstract
A new 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 new 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. By a slight modification the proposed estimator can also be used in the case when the parameters of the driving noise probability density function are not known.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 1986
- Accession Number
- ADA175395
Entities
People
- Debasis Sengupta
- Steven Kay
Organizations
- University of Rhode Island