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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 theoretical foundation of the method and verify this method
by simulations. The method is based on pivoting the Hessian matrix by
Gau\ss-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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Keywords

AIC, Akaike criterion, AR, autoregressive processes, composite hypothesis,
maximum likelihood, model order, system identification, time series
analysis.

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Published

*An efficient algorithm for selecting optimal configurations of
AR-coefficients*
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BibTeX

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