Maximum likelihood estimation of the parameters of nonminimum phase and noncausal ARMA models

Klaus Bolding Rasmussen

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Abstract

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
Original languageEnglish
JournalI E E E Transactions on Signal Processing
Volume42
Issue number1
Pages (from-to)209-211
ISSN1053-587X
DOIs
Publication statusPublished - 1994

Bibliographical note

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