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- W2138996910 abstract "The quest in autoregressive model order selection is for the model with smallest prediction error. The possibilities of selecting a suboptimal order can be divided in overfitting and underfitting. In order selection criteria based on asymptotical large sample theory as well as in their finite sample counterparts, the penalty factor /spl alpha/ can be considered as the balancing factor between overfit and underfit. An optimization of the value for the penalty factor is only effective, after a correction for the statistics of the finite observation length has been carried out, by using the results of the finite sample theory. A theoretical treatment is in asymptotic theory based on the true AR process order. To apply the reasonings to the practical situations, where only a finite number of observations has been measured, the optimal model order is introduced. It is defined as the order with lowest expected prediction error. >" @default.
- W2138996910 created "2016-06-24" @default.
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- W2138996910 date "2002-12-17" @default.
- W2138996910 modified "2023-09-27" @default.
- W2138996910 title "The influence of the penalty factor in order selection criteria" @default.
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- W2138996910 doi "https://doi.org/10.1109/imtc.1994.352172" @default.
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