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  Far East Journal of Theoretical Statistics  
 ISSN: 0972-0863
 
 
 

     Far East Journal of Theoretical Statistics
    Volume 25, Issue 1, Pages 51 - 72 (May 2008)


SELECTION CRITERIA BASED ON MONTE CARLO SIMULATION AND CROSS VALIDATION IN MIXED MODELS

Junfeng Shang (U. S. A.)

Received October 17, 2007

References:



[1] H. Akaike, Information theory and an extension of the maximum likelihood principle, 2nd International Symposium on Information Theory, B. N. Petrov and F. Csaki, eds., pp. 267-281, Akademia Kiado, Budapest, 1973.

[2] H. Akaike, A new look at the statistical model identification, IEEE Trans. Automat. Control AC-19 (1974), 716-723.

[3] R. Azari, L. Li and C. L. Tsai, Longitudinal data model selection, Comput. Statist. Data Anal. 50 (2006), 3053-3066.

[4] S. L. Davies, A. A. Neath and J. E. Cavanaugh, Cross validation model selection criteria for linear regression based on the Kullback-Leibler discrepancy, Stat. Methodol. 2 (2005), 249-266.

[5] C. M. Hurvich, R. H. Shumway and C. L. Tsai, Improved estimators of Kullback-Leibler information for auto regressive model selection in small samples, Biometrika 77 (1990), 709-719.

[6] C. M. Hurvich and C. L. Tsai, Regression and time series model selection in small samples, Biometrika 78 (1989), 499-509.

[7] A. D. R. McQuarrie and C. L. Tsai, Regression and Time Series Model Selection, World Scientific, River Edge, New Jersey, 1998.

[8] J. Shang and J. E. Cavanaugh, Bootstrap variants of the Akaike information criterion for mixed model selection, Comput. Statist. Data Anal. 52 (2008), 2004-2021.

[9] R. Shibata, Bootstrap estimate of Kullback-Leibler information for model selection, Statist. Sinica 7 (1997), 375-394.

[10] N. Sugiura, Further analysis of the data by Akaike’s information criterion and the finite corrections, Commun. Statist. Theory Methods 7 (1978), 13-26.

Keywords and phrases: Akaike information criterion (AIC), improved AIC (AICi), predictive divergence criterion (PDC), Kullback-Leibler discrepancy.

 


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