Empiricial comparison between some model selection criteria

Communications in Statistics: Simulation and Computation

Volumn 40, Issue 1, 2010, Pages 72-86

  Sayyareh, Abdolreza ^a   Obeidi, Raouf ^a   Bar Hen, Avner ^b,c  

^a RAZI UNIVERSITY   (Iran)

^b UNIVERSITÉ PARIS DESCARTES   (France)

^c EHESP SCHOOL OF PUBLIC HEALTH   (France)

Author keywords

Akaike information criterion; Bayesian information criterion; Cox's test; Hypothesis testing; Kullback Leibler; Mis specified models; Model selection; Non nested models; Vuong's test

Indexed keywords

STATISTICS;

AKAIKE INFORMATION CRITERION; BAYESIAN INFORMATION CRITERION; HYPOTHESIS TESTING; KULLBACK-LEIBLER; MODEL SELECTION;

STATISTICAL TESTS;

EID: 82255175704     PISSN: 03610918     EISSN: 15324141     Source Type: Journal    
DOI: 10.1080/03610918.2010.530367     Document Type: Article

References (27)

1. Akaike, H. (1973). Information theory and an extension of maximum likelihood principle. Second International Symposium on Information Theory. Akademia Kiado, pp. 267-281.
2. Atkinson, A. C. (1970). A method for discriminating between models. Journal of the Royal Statistical Society B 32:323-344.
3. Bozdogan, H. (2000). Akaike's information criterion and recent development in information complexity. Journal of Mathematical Psychology 44:62-91.
4. Commenges, D., Sayyareh, A., Letenneur, L., Guedj, J., Bar-Hen, A. (2008). Estimating a difference of Kullback-Leibler risks using a normalized difference of AIC. The Annals of Applied Statistics 2(3):1123-1142.
5. Cox, D. R. (1961). Test of separate families of hypothesis. Proceeding of the Fourth Berkeley Symposium on Mathematical Statistics and Probability 1:105-123.
6. Cox, D. R. (1962). Further result on tests of separate families of hypothesis. Journal of the Royal Statistical Society B 24:406-424.
7. Dastoor, N. K. (1983). Some aspects of testing non nested hypothesis. Journal of Econometrica 21:213-228.
8. Davidson, R., MacKinnon, J. G. (1981). Several tests for model specification in the presence of alternative hypotheses. Econometrica 49:781-793.
9. Davidson, R., MacKinnon, J. G. (2002). Bootstrap J tests of nonnested linear regression models. Journal of Econometrics 109:167-193.
10. de Leeuw, J. (1992). Introduction to Akaike (1973) information theory and an extension of the maximum likelihood principle. In: Kotz, S., Johnson, N. L., eds. Breakthroughs in Statistics. New York: Springer, pp. 599-609.
11. Genius, M., Strazzera, E. (2002). A note about model selection and tests for non nested contingent valuation models. Economic Letters 74:363-370.
12. Fisher, G. R., McAleer, M. (1981). Alternative procedures and associated tests of significance for non nested hypotheses. Journal of Econometrics 16:103-119.
13. Huber, P. J. (1967). The behavior of maximum likelihood estimate under nonstandard conditions. Proc. Fifth Berkeley Symposium in Mathematical Statistics and Probability. Berkeley, University of California Press, pp. 221-233.
14. Kass, R. E., Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association 90:773-795.
15. Kullback, S., Leibler, R. A. (1951). On information and sufficiency. Annals of Mathematical Statistics 22:79-86.
16. Massart, P. (2007). Ecole d'été de probabilités de Saint-Flour 2003. Lecture Notes in Mathematics 1896, Berlin/Heidelberg: Springer.
17. Pesaran, M. H. (1974). On the general test of model selection. Review of Economic Studies 41:153-171.
18. Pesaran, M. H., Deaton, A. S. (1978). Testing non nested nonlinear regression models. Econometrica 46:667-694.
19. Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6:461-464.
20. Shibata, R. (1983). Asymptotic mean efficiency of a selection of regression variables. Annals of the Institute of Statistical Mathematics 35:415-423.
21. Shimodiara, H. (1998). An application of multiple comparison techniques to model selection. Annals of Institute Statistical Mathematics 50(1):1-13.
22. Shimodaira, H. (2001). Multiple comparisons of log-likelihoods and combining non nested models with application to phylogenetic tree selection. Communications in Statistics 30:1751-1772.
23. Vuong, Q. H. (1989). Likelihood ratio tests for model selection and non nested hypotheses. Econometrica 57(2):307-333.
24. Yanagihara, H., Ohomoto, C. (2005). On distribution of AIC in linear regression models. Journal of Statistical Planning and Inference 133:417-433.
25. Wald, A. (1949). Note on consistency of maximum likelihood estimate. Annals of Mathematical Statistics 60:595-603.
26. White, H. (1982a). Maximum likelihood estimation of misspecified models. Econometrica 50:1-26.
27. White, H. (1982b). Regularity conditions for Cox's test of non nested hypotheses. Journal of Econometrics 19:301-318.