Finite Sample FPE and AIC Criteria for Autoregressive Model Order Selection Using Same-Realization Predictions

被引：0

作者：

Shapoor Khorshidi

Mahmood Karimi

机构：

[1] Shiraz University,Department of Electrical Engineering

来源：

EURASIP Journal on Advances in Signal Processing | / 2009卷

关键词：

Covariance; Information Technology; Simulated Data; Prediction Error; Quantum Information;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

A new theoretical approximation for expectation of the prediction error is derived using the same-realization predictions. This approximation is derived for the case that the Least-Squares-Forward (LSF) method (the covariance method) is used for estimating the parameters of the autoregressive (AR) model. This result is used for obtaining modified versions of the AR order selection criteria FPE and AIC in the finite sample case. The performance of these modified criteria is compared with other same-realization AR order selection criteria using simulated data. The results of this comparison show that the proposed criteria have better performance.

引用

共 19 条

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Khorshidi, Shapoor
Karimi, Mahmood
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