Efficient Estimation of Parameters for Non-Gaussian Autoregressive Processes

Authors

Debasis Sengupta, University of California, Santa Barbara
Steven Kay, University of Rhode Island

Document Type

Article

Date of Original Version

1-1-1989

Abstract

The problem of estimating the parameters of a non-Gaussian autoregressive process is addressed. Departure of the driving noise from Gaussianity is shown to have the potential for improving the accuracy of the estimation of the parameters. While the standard linear prediction techniques are computationally efficient, they show a substantial loss of efficiency when applied to non-Gaussian processes. A maximum likelihood estimator is proposed for more precise estimation of the parameters of these processes coupled with a realistic non-Gaussian model for the driving noise. The performance is compared to that of the linear prediction estimator and, as expected, the maximum likelihood estimator displays a marked improvement. © 1989 IEEE

Publication Title, e.g., Journal

IEEE Transactions on Acoustics, Speech, and Signal Processing

Volume

37

Issue

6

Citation/Publisher Attribution

Sengupta, Debasis, and Steven Kay. "Efficient Estimation of Parameters for Non-Gaussian Autoregressive Processes." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, 6 (1989): 785-794. doi: 10.1109/ASSP.1989.28052.