Simple and Efficient Estimation of Parameters of Non-Gaussian Autoregressive Processes
Rept. no. 4, Aug 1984-Jul 1986
RHODE ISLAND UNIV KINGSTON DEPT OF ELECTRICAL ENGINEERING
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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.
- Statistics and Probability