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Original Articles

A modified information criterion for model selection

Pages 2710-2721 | Received 25 Feb 2019, Accepted 18 Dec 2019, Published online: 30 Dec 2019

References

  • Akaike, H. 1973. Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60 (2):255–65. doi:10.1093/biomet/60.2.255.
  • Bollen, K. A., S. Ray, J. Zavisca, and J. J. Harden. 2012. A comparison of Bayes factor approximation methods including two new methods. Sociological Methods & Research 41 (2):294–24. doi:10.1177/0049124112452393.
  • Bozdogan, H. 1987a. ICOMP: A new model-selection criterion. Paper presented at the 1. Conference of the International Federation of Classification Societies.
  • Bozdogan, H. 1987b. Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika 52 (3):345–70. doi:10.1007/BF02294361.
  • Bozdogan, H. 1994. Mixture-model cluster analysis using model selection criteria and a new informational measure of complexity. In Proceedings of the first US/Japan conference on the frontiers of statistical modeling: An informational approach, 69–113. Dordrecht: Springer.
  • Bozdogan, H. 2000. Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology 44 (1):62–91. doi:10.1006/jmps.1999.1277.
  • Chen, J., and Z. Chen. 2012. Extended BIC for small-n-large-P sparse GLM. Statistica Sinica 22:555–74. doi:10.5705/ss.2010.216.
  • Friedman, J., T. Hastie, and R. Tibshirani. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33 (1):1. doi:10.18637/jss.v033.i01.
  • Haff, L. (1980). Empirical Bayes estimation of the multivariate normal covariance matrix. The Annals of Statistics 3 (8):586–597. doi:10.1214/aos/1176345010.
  • Hannan, E. J., and B. G. Quinn. 1979. The determination of the order of an autoregression. Journal of the Royal Statistical Society: Series B (Methodological) 41 (2):190–5. doi:10.1111/j.2517-6161.1979.tb01072.x.
  • Hirose, K., S. Tateishi, and S. Konishi, 2013. Tuning parameter selection in sparse regression modeling. Computational Statistics & Data Analysis 59:28–40. doi:10.1016/j.csda.2012.10.005.
  • Hui, F. K., D. I. Warton, and S. D. Foster. 2015. Tuning parameter selection for the adaptive lasso using ERIC. Journal of the American Statistical Association 110 (509):262–9. doi:10.1080/01621459.2014.951444.
  • Hurvich, C. M., and C.-L. Tsai. 1991. Bias of the corrected AIC criterion for underfitted regression and time series models. Biometrika 78 (3):499–509. doi:10.2307/2337019.
  • Koc, E. K., and H. Bozdogan. 2015. Model selection in multivariate adaptive regression splines (MARS) using information complexity as the fitness function. Machine Learning 101 (1–3):35–58. doi:10.1007/s10994-014-5440-5.
  • Konishi, S., and G. Kitagawa. 2008. Information criteria and statistical modeling. New York: Springer Science & Business Media.
  • Pamukçu, E., H. Bozdogan, and S. Çalık. 2015. A novel hybrid dimension reduction technique for undersized high dimensional gene expression data sets using information complexity criterion for cancer classification. Computational and Mathematical Methods in Medicine 2015:1–14. doi:10.1155/2015/370640.
  • Rossi, P., and M. P. Rossi. 2013. Package ‘PERregress’.
  • Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6 (2):461–4. doi:10.1214/aos/1176344136.
  • Team, R. C. 2018. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/
  • Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological) 58 ( 1):267–88. doi:10.1111/j.2517-6161.1996.tb02080.x.
  • Wei, C.-Z. 1992. On predictive least squares principles. The Annals of Statistics 20 ( 1):1–42. doi:10.1214/aos/1176348511.
  • Żak-Szatkowska, M., and M. Bogdan. 2011. Modified versions of the Bayesian information criterion for sparse generalized linear models. Computational Statistics & Data Analysis 55 (11): 2908–2924. doi:10.1016/j.csda.2011.04.016.

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