FINITE SAMPLE AIC FOR AUTOREGRESSIVE MODEL ORDER SELECTION

被引：0
|
作者：
Karimi, Mahmood [1 ]
机构：
[1] Shiraz Univ, Sch Engn, Dept Elect Engn, Shiraz, Iran
来源：
ICSPC: 2007 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS, VOLS 1-3, PROCEEDINGS | 2007年
关键词：
Autoregressive processes; Information theory; Modeling;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
An estimate for the prediction error of the least-squares-forward (LSF) autoregressive (AR) parameter estimation method has been recently proposed. In this paper, this estimate is used for deriving a new AR model order selection criterion. This new criterion is an estimate of the Kullback-Leibler index and can replace the Akaike information criterion (AIC) and its corrected version AICC. In a simulation study, the performance of this new criterion and other existing order selection criteria is examined in the finite sample case. Simulation results show that the performance of the proposed criterion is much better than the other theoretically derived criteria.
引用
收藏
页码：1219 / 1222
页数：4
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