An Efficient Algorithm for Selecting Optimal Configurations of Ar-coefficients
نویسنده
چکیده
There exists an essential diierence between the correct Auto Regressive (AR) model and the optimal AR-model. We try to nd an optimal model balancing between exibility, using many AR-parameters, and low variance, using only a few AR-parameters. We select an optimal AR-parameter con-guration consisting of zero and non-zero parameters given a maximum AR-order. This optimal connguration will be selected using a Modiied 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 veriication by simulations. The method is based on pivoting the Hessian matrix by Gauu-Jordan pivots. As a result we can now select an optimal parameter connguration with an a priori probability of selecting a connguration with a too large number of parameters given an a priori selected maximum AR-order.
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Moddemeijer: an Efficient Algorithm for Selecting Optimal Configurations of Ar-coefficients
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