On information criteria and the generalized likelihood ratio test of model order selection

IEEE Signal Processing Letters

Volumn 11, Issue 10, 2004, Pages 794-797

  Stoica, Petre ^a   Selén, Yngve ^a   Li, Jian ^b  

^a UPPSALA UNIVERSITY   (Sweden)

^b University of Florida   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; INFORMATION THEORY; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION; MONTE CARLO METHODS; OPTIMIZATION; POLES AND ZEROS; WHITE NOISE;

AKAIKE INFORMATION CRITERION (AIC); GENERALIZED LIKELIHOOD RATIO TEST (GLRT); INFORMATION CRITERIA; MODEL SELECTION; SPARSE MODELS;

SIGNAL DETECTION;

EID: 4744369980     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2004.835468     Document Type: Article

References (7)

1. P. Stoica and Y. Selén, "Model order selection - A review of the AIC, GIC, and BIC rules," IEEE Signal Processing Mag., vol. 21, pp. 36-47, July 2004.
2. C. Gourieroux and A. Monfort, Statistics and Econometric Models. Cambridge, U.K.: Cambridge Univ. Press, 1995, vol. I and II.
3. S. M. Kay, Fundamentals of Statistical Signal Processing. Upper Saddle River, NJ: Prentice-Hall, 1998, vol. 2. Detection Theory.
4. T. Söderström, "On model structure testing in system identification," Int. J. Control, vol. 26, no. 1, pp. 1-18, 1977.
5. B. M. Pötscher, "Effects of model selection on inference," Econom. Theory, vol. 7, pp. 163-185, 1991.
6. K. P. Burnham and D. R. Anderson, Model Selection and Multi-Model Inference. New York: Springer, 2002.
7. H. Gu, "Estimating the number of signals and signal resolution," IEEE Trans. Signal Processing, vol. 46, pp. 2267-2270,