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An efficient algorithm for selecting optimal configurations of
AR-coefficients

University of Groningen, Department of Computing Science,

P.O. Box 800, NL-9700 AV Groningen, The Netherlands,

phone: +31.50.363 3940 - fax: +31.50.363 38005
- e-mail: rudy@cs.rug.nl

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Abstract

There exists an essential difference between the correct Auto
Regressive (AR) model and the optimal AR-model. We try to find an optimal
model balancing between flexibility, using many AR-parameters, and low
variance, using only a few AR-parameters. We select an optimal AR-parameter
configuration consisting of zero and non-zero parameters given a maximum
AR-order. This optimal configuration will be selected using a Modified
Information Criterion (MIC) which is closely related to Akaike's criterion
(AIC). This MIC allows an a priori selection of the probability of
estimating too many parameters.
We present the method and a verification by simulations. The method is based
on pivoting the Hessian matrix by Gauss-Jordan pivots. As a result we can
now select an optimal parameter configuration with an a priori probability
of selecting a configuration with a too large number of parameters given an
a priori selected maximum AR-order.

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Published

Twentieth Symposium on Information Theory in the Benelux,
May 27-28, 1999, Haasrode, Belgium,
pp 189-196,
eds. A. Barbé et. al.,
Werkgemeenschap Informatie- en
Communicatietheorie, Enschede, The Netherlands,
and
IEEE Benelux Chapter on Information Theory,
ISBN 90-71048-14-4
BibTeX

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