Autoregressive Order Estimation Combined with Pruning of the Coefficients
نویسنده
چکیده
A correctly derived Auto Regressive (AR) model can not always optimize the intended approximation. An optimal model should balance bias, caused by under-fitting, and additional variance, caused by over-fitting. The selection of this optimal AR-model is a combination of AR-order estimation and the reduction of the number of coefficients by pruning. We leave the classical approach of ARorder estimation and replace it by AR-coefficient selection. As a selection criterion we use the Modified Information Criterion (MIC), which is closely related to Akaike’s criterion (AIC) and has a guaranteed a priori chosen over-fitting probability. We present our algorithm and its verification by simulations.
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