Browse > Article
http://dx.doi.org/10.5351/KJAS.2017.30.1.119

Using the corrected Akaike's information criterion for model selection  

Song, Eunjung (Department of Statistics, Inha University)
Won, Sungho (Department of Public Health Science, Seoul National University)
Lee, Woojoo (Department of Statistics, Inha University)
Publication Information
The Korean Journal of Applied Statistics / v.30, no.1, 2017 , pp. 119-133 More about this Journal
Abstract
Corrected Akaike's information criterion (AICc) is known to have better finite sample properties. However, Akaike's information criterion (AIC) is still widely used to select an optimal prediction model among several candidate models due to of a lack of research on benefits obtained using AICc. In this paper, we compare the performance of AIC and AICc through numerical simulations and confirm the advantage of using AICc. In addition, we also consider the performance of quasi Akaike's information criterion (QAIC) and the corrected quasi Akaike's information criterion (QAICc) for binomial and Poisson data under overdispersion phenomenon.
Keywords
AIC; AICc; QAIC; QAICc; overdispersion;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In 2nd International Symposium on Information Theory (pp. 267-281), Akademia Kiado, Budapest.
2 Bloom, M. and Milkovich, G. T. (1998). Relationships among risk, incentive pay, and organizational performance, Academy of Management Journal, 41, 283-297.   DOI
3 Burnham, K. P. and Anderson, D. (2003). Model Selection and Multi-Model Inference: a Practical Informatio-Theoric Approach, Springer, New York.
4 Cavanaugh, J. E., Davies S. L., and Neath, A. A. (2008). Discrepancy-based model selection criteria using cross-validation. In Statistical Models and Methods for Biomedical and Technical Systems (pp. 473-486), Birkhauser, Boston.
5 Debrock, C., Preux, P. M., Houinato, D., Druet-Cabanac, M., Kassa, F., Adjien, C., Avode, G., Denis, F., Boutros-Toni, F., and Dumas, M. (2000). Estimation of the prevalence of epilepsy in the Benin region of Zinvie using the capture-recapture method, International Journal of Epidemiology, 29, 330-335.   DOI
6 Harada, T., Ariyoshi, N., Shimura, H., Sato, Y., Yokoyama, I., Takahashi, K., Yamagata, S., Imamaki, M., Kobayashi, Y., Ishii, I., Miyazaki, M., and Kitada, M. (2010). Application of Akaike information criterion to evaluate warfarin dosing algorithm, Thrombosis Research, 126, 183-190.   DOI
7 Hinde, J. and Demetrio, C. G. B. (2007). Overdispersion: models and estimation. In A Short Course for 13th Brazilian Symposium of Probability and Statistics (SINAPE 1998), Brazil.
8 Hurvich, C. M. and Tsai, C. L. (1989). Regression and time series model selection in small samples, Biometrika, 76, 297-307.   DOI
9 Johnson, R. J., Kerr, C. L., Enouri, S. S., Modi, P., Lascelles, B. D. X., and Castillo, J. R. E. (2016). Pharmacoki-netics of liposomal encapsulated buprenorphine suspension following subcutaneous administration to cats, Journal of Veterinary Pharmacology and Therapeutics, Available from: http://dx.doi.org/10.1111/jvp.12357   DOI
10 Kim, H. J., Cavanaugh, J. E., Dallas, T. A., and Fore, S. A. (2014). Model selection criteria for overdispersed data and their application to the characterization of a host-parasite relationship, Environmental and Ecological Statistics, 21, 329-350.   DOI
11 Lebreton, J. D., Burnham, K. P., Clobert, J., and Anderson, D. R. (1992). Modeling survival and testing biological hypotheses using marked animals: a uni ed approach with case studies, Ecological Monograph, 62, 67-118.   DOI
12 McDonald, G. C. and Schwing, R. C. (1973). Instabilities of regression estimates relating air pollution to mortality, Technometrics, 15, 463-481.   DOI
13 Shmueli, G. (2010). To explain or to predict?, Statistical Science, 25, 289-310.   DOI
14 Takeuchi, K. (1976). Distribution of informational statistics and a criterion of model fitting, Suri-Kagaku (Mathematic Sciences), 153, 12-18.
15 Zampetakis, L. A., Bouranta, N., and Moustakis, V. S. (2010). On the relationship between individual creativity and time management, Thinking Skills and Creativity, 5, 23-32.   DOI