Statistical efficiency of the sample autocorrelation function in ARMA parameter estimation
- 24 March 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 7, 240-243
- https://doi.org/10.1109/icassp.1982.1171619
Abstract
Many modern ARMA spectral estimators are based either on the raw data or on some version of the lagged-product sample autocorrelation function (ACF). These two classes are compared in terms of their Cramer-Rao bound generalized variances in estimating the poles and zeros of the ARMA system generating the process. It is seen that the choice of lags of the sample ACF required to preserve most of the information in the data is signal dependent. Recommendations of a "good" information-preserving choice of lags for an AR(2) process in white noise are tabulated against pole magnitude and SNR. The case of two additive narrowband AR(2) processes is also studied. Author(s) Bruzzone, S.P. University of Minnesota, Minneapolis, Minnesota Kaveh, M.Keywords
This publication has 4 references indexed in Scilit:
- Statistical analysis of a spectral estimator for ARMA processesIEEE Transactions on Automatic Control, 1980
- A note on covariance-invariant digital filter design and autoregressive moving average spectrum analysisIEEE Transactions on Acoustics, Speech, and Signal Processing, 1979
- Estimation of the autoregressive parameters of a mixed autoregressive moving-average time seriesIEEE Transactions on Automatic Control, 1970
- On the Sufficient Statistics for Stationary Gaussian Random ProcessesTheory of Probability and Its Applications, 1961