Journal article
Maximum likelihood estimation of the parameters of nonminimum phase and noncausal ARMA models
The well-known prediction-error-based maximum likelihood (PEML) method can only handle minimum phase ARMA models. This paper presents a new method known as the back-filtering-based maximum likelihood (BFML) method, which can handle nonminimum phase and noncausal ARMA models. The BFML method is identical to the PEML method in the case of a minimum phase ARMA model, and it turns out that the BFML method incorporates a noncausal ARMA filter with poles outside the unit circle for estimation of the parameters of a causal, nonminimum phase ARMA model
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 1994 |
| Pages: | 209-211 |
| ISSN: | 19410476 and 1053587x |
| Types: | Journal article |
| DOI: | 10.1109/78.258141 |
Autoregressive processes Equations Filters Maximum likelihood estimation Parameter estimation Phase estimation Poles and zeros Predictive models Stochastic processes Vectors back-filtering-based maximum likelihood method filtering and prediction theory maximum likelihood estimation minimum phase ARMA models noncausal ARMA models nonminimum phase ARMA models parameter estimation poles poles and zeros prediction-error-based maximum likelihood method signal processing stochastic processes stochastic signal model time series
