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 p th-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
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