DocumentCode :
797524
Title :
On the relationship of maximum likelihood sampled-data power spectrum identification and optimum predicition filters
Author :
Tretter, Steven A.
Author_Institution :
University of Maryland, College Park, MD, USA
Volume :
13
Issue :
3
fYear :
1968
fDate :
6/1/1968 12:00:00 AM
Firstpage :
303
Lastpage :
304
Abstract :
Methods for estimating the sampled power spectral density of a stochastic process in terms of a rational function of 
have been presented in the literature. A method based on the maximum likelihood criterion for Gaussian processes leads to the minimum residual criterion.[1],[2]This correspondence points out the relationship of the minimum residual criterion to optimum prediction filters and justifies the use of the criterion even for non-Gaussian processes.
have been presented in the literature. A method based on the maximum likelihood criterion for Gaussian processes leads to the minimum residual criterion.[1],[2]This correspondence points out the relationship of the minimum residual criterion to optimum prediction filters and justifies the use of the criterion even for non-Gaussian processes.Keywords :
Matrix inversion; Stochastic processes; maximum-likelihood (ML) estimation; Adaptive systems; Autocorrelation; Density functional theory; Digital filters; Educational institutions; Gaussian processes; Maximum likelihood estimation; Parameter estimation;
fLanguage :
English
Journal_Title :
Automatic Control, IEEE Transactions on
Publisher :
ieee
ISSN :
0018-9286
Type :
jour
DOI :
10.1109/TAC.1968.1098907
Filename :
1098907
Link To Document :
