Title :
A modified akaike information criterion
Author_Institution :
University of Minnesota, Minneapolis, Minnesota
Abstract :
A method, closely related to Akaike´s Information Criterion (AIC), is introduced that more nearly matches practical methods of estimating the parameters of an autoregressive (AR) model of a stationary time series. The method is computationally similar to AIC, and in preliminary experiments has shown considerable success in identifying AR model orders.
Keywords :
Autoregressive processes; Equations; Gaussian processes; Maximum likelihood estimation; Signal processing; Technological innovation;
Conference_Titel :
Decision and Control including the 17th Symposium on Adaptive Processes, 1978 IEEE Conference on
Conference_Location :
San Diego, CA, USA
DOI :
10.1109/CDC.1978.268071
