DocumentCode :
851882
Title :
A necessary and sufficient condition for the existence of the maximum likelihood estimate in autoregressive models
Author :
Degerine, Serge
Author_Institution :
LMC, Univ. Joseph Fourier, Grenoble, France
Volume :
41
Issue :
2
fYear :
1993
fDate :
2/1/1993 12:00:00 AM
Firstpage :
988
Lastpage :
990
Abstract :
The author studies the existence of a maximum likelihood estimate (MLE) for the parameters of a pth-order autoregressive (AR) model from n⩾1 independent records of length m of a complex time series. It is shown that, for almost all such set of observations, the MLE exists if and only if the n records be exactly fitted by complex undamped sinusoids using the same set of p distinct frequencies
Keywords :
maximum likelihood estimation; signal processing; time series; AR model; MLE; autoregressive model; autoregressive models; complex time series; complex undamped sinusoids; frequencies; independent records; maximum likelihood estimate; necessary condition; signal processing; sufficient condition; Array signal processing; Correlation; Covariance matrix; Frequency; Linear systems; Maximum likelihood estimation; Parameter estimation; Relaxation methods; Signal processing; Sufficient conditions;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.193241
Filename :
193241
Link To Document :
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