Multivariate regression model selection from small samples using Kullback's symmetric divergence

Signal Processing

Volumn 86, Issue 8, 2006, Pages 2074-2084

  Seghouane, Abd Krim ^a,b  

^a CSIRO   (Australia)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

AIC; KIC; KICc; Kullback Leibler information; Model selection; Multivariate regression models

Indexed keywords

ASYMPTOTIC STABILITY; ESTIMATION; MATHEMATICAL MODELS; VECTORS;

AIC; KULLBACK-LEIBLER INFORMATION; MODEL SELECTION; MULTIVARIATE REGRESSION MODEL;

REGRESSION ANALYSIS;

EID: 33744538807     PISSN: 01651684     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sigpro.2005.10.009     Document Type: Article

References (21)

1. Bedrik E.J., and Tsai C.L. Model selection for multivariate regression in small samples. Biometrics 50 (1994) 226-231
2. Kullback S., and Leibler R.A. On information and sufficiency. Ann. Math. Statist. 22 (1951) 76-86
3. Akaike H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19 (1974) 716-723
4. Jeffreys H. An invariant form of the prior probability in estimation problems. J. Roy. Statist. Soc. A (1946) 453-469
5. Cavanaugh J.E. A large-sample model selection criterion based on Kullback's symmetric divergence. Statist. Probab. Lett. 42 (1999) 333-343
6. Schwarz G. Estimating the dimension of a model. Ann. Statist. 6 (1978) 461-464
7. Rissanen J. Modeling by shortest data description. Automatica 14 (1978) 465-471
8. Seghouane A.K., and Bekara M. A small sample model selection criterion based on the Kullback symmetric divergence. IEEE Trans. Signal Process. 52 (2004) 3314-3323
9. Cavanaugh J.E. Criteria for linear model selection based on Kullback's symmetric divergence. Austral. New Zealand J. Statist. 46 (2004) 257-274
10. Hurvich C.M., and Tsai C.L. Regression and time series model selection in small samples. Biometrika 76 (1989) 297-307
11. Seghouane A.K., Bekara M., and Fleury G. A criterion for model selection in the presence of incomplete data based on Kullback's symmetric divergence. Signal Processing 85 (2005) 1405-1417
12. Shimodaira H. In: Cheenseman P., and Oldford R.W. (Eds). A New Criterion for Selecting Models from Partially Observed Data vol. 89 (1994), Springer, Berlin 21-29
13. Cavanaugh J.E., and Shumway R.H. An Akaike information criterion for model selection in the presence of incomplete data. J. Statist. Plann. Inference 67 (1998) 45-65
14. A.K. Seghouane, A criterion for vector, autoregressive model selection based on Kullback's symmetric divergence, in: Proceedings of ICASSP '05, 2005, pp. 97-100.
15. Anderson T.W. An Introduction to Multivariate Statistical Analysis. third ed. (2003), Wiley, Hoboken, New Jersey
16. Bernardo J.M. Psi (digamma) function. Appl. Statist. 25 (1976) 315-317
17. Akaike H. Statistical predictor identification. Ann. Inst. Statist. Math. 22 (1970) 203-217
18. Bhansali R.J., and Downham D.Y. Some properties of the order of an autoregressive model selected by a generalization of Akaike's FPE criterion. Biometrika 64 (1997) 547-551
19. Broersen P.M.T., and Wensink H.E. On the penalty factor for autoregressive order selection in finite samples. IEEE Trans. Signal Process. 44 (1996) 748-752
20. Muirhead R.J. Aspects of Multivariate Statistical Theory (1982), Wiley, New York
21. S. Kotz, N.L. Johnson, C.B. Read (Eds.), Encyclopedia of Statistical Sciences, vol. 2, Wiley, New York, 1982.