NDLI: Optimal Choice of AR and MA Parts in Autoregressive Moving Average Models

Content Provider	IEEE Xplore Digital Library
Author	Kashyap, Rangasami L.
Copyright Year	1979
Abstract	This paper deals with the Bayesian method of choosing the best model for a given one-dimensional series among a finite number of candidates belonging to autoregressive (AR), moving average (MA), ARMA, and other families. The series could be either a sequence of observations in time as in speech applications, or a sequence of pixel intensities of a two-dimensional image. The observation set is not restricted to be Gaussian. We first derive an optimum decision rule for assigning the given observation set to one of the candidate models so as to minimize the average probability of error in the decision. We also derive an optimal decision rule so as to minimize the average value of the loss function. Then we simplify the decision rule when the candidate models are different Gaussian ARMA models of different orders. We discuss the consistency of the optimal decision rule and compare it with the other decision rules in the literature for comparing dynamical models.
Sponsorship	IEEE Computer Society
Starting Page	99
Ending Page	104
Page Count	6
File Size	1431926
File Format	PDF
ISSN	01628828
Volume Number	PAMI-4
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1982-03-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Autoregressive processes Bayesian methods Pixel Speech analysis Data compression Predictive models Machine intelligence Image texture Shape Image analysis speech models ARMA models Bayesian approach image models model choice
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Artificial Intelligence Computational Theory and Mathematics Computer Vision and Pattern Recognition Software

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