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If P is unknown, it can be selected using some model order selection criterion [38].
It is different from model order selection criteria in the sense that the selected non-zero AR coefficients do not necessarily have to be consecutive.
Different model order selection criteria are comparatively discussed, and the modeling procedure is considered to improve the calculation efficiency.
To do so, we will select the L most relevant signatures using model order selection techniques [30].
In Section 3, different model order selection criteria are comparatively discussed, and ARMA(2n,2n – 1) modeling procedures are adopted to optimize the order selection path.
In addition, the model order selection is achieved by the optimization scheme.
The identification scheme is extended with a proposed algorithm for the model order selection.
Particular attention is focused on two parts of this process, namely model order selection and model tuning.
Under quite realistic experimental conditions, the new estimators outperform classical prediction error methods also when the latter are equipped with an oracle for model order selection.
In this case, the problem of model order selection is equivalent to the estimation of the multiplicity of the smallest eigenvalues of the data covariance matrix.
model order selection.
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