Estimation of the order of autoregressive process

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Abstract
Though there are several methods for estimating the order of the autoregressive process, it is hoped that it can be estimated with high sensitivity. In this paper, an approach based on an information measure which characterizes the autoregressive process has boon made to the estimation of the order of the process. A mixing operator, which is found by adding an independent random variable to the sub-sequence of the original data sequence, is introduced. By applying the operator to the conditional entropies, we developed a statistic which estimates the order of the autoregressive process. Results of computer simulation are presented to verify and compare this algorithm with other methods.

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