364
Views
3
CrossRef citations to date
0
Altmetric
Original Articles

Variable selection in linear regression analysis with alternative Bayesian information criteria using differential evaluation algorithm

, , &
Pages 605-614 | Received 14 Mar 2016, Accepted 23 Jan 2017, Published online: 05 Jun 2017

References

  • Akaike, H. (1973). Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60(2):255–265.
  • Akaike, H. (1974). A new look at the statistical model identification. Automatic Control, IEEE Transactions on 19(6):716–723.
  • Akaike, H. (1980). Likelihood and the Bayes procedure. Trabajos de estadística y de investigación operativa 31(1):143–166.
  • Ardia, D., Boudt, K., Carl, P., Mullen, K. M., Peterson, B. G. (2011). Differential evolution with deoptim. R Journal 3(1):27–34.
  • Bollen, K. A., Ray, S., Zavisca, J., Harden, J. J. (2012). A comparison of Bayes factor approximation methods including two new methods. Sociological Methods & Research 41(2):294–324.
  • Bozdogan, H. (1987). Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions. Psychometrika 52(3):345–370.
  • Bozdogan, H. (2000). Akaike's information criterion and recent developments in information complexity. Journal of Mathematical Psychology 44(1):62–91.
  • Bozdogan, H. (2004). Intelligent statistical data mining with information complexity and genetic algorithms. Statistical Data Mining and Knowledge Discovery 15–56.
  • Brusco, M. J. (2014). A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis. Computational Statistics and Data Analysis 77:38–53.
  • Cavanaugh, J. E. (2009). 171: 290 model selection lecture VI: The Bayesian information criterion. University Lecture.
  • Creel, M., Kristensen, D. (2016). On selection of statistics for approximate Bayesian computing (or the method of simulated moments). Computational Statistics and Data Analysis 100:99–114.
  • Drezner, Z., Marcoulides, G. A., Salhi, S. (1999). Tabu search model selection in multiple regression analysis. Communications in Statistics-Simulation and Computation 28(2):349–367.
  • Hurvich, C. M., Tsai, C. L. (1989). Regression and time series model selection in small samples. Biometrika 76(2):297–307.
  • Hwang, J. S., Hu, T. H. (2015). A stepwise regression algorithm for high-dimensional variable selection. Journal of Statistical Computation and Simulation 85(9):1793–1806.
  • Koc, E. K., Bozdogan, H. (2015). Model selection in multivariate adaptive regression splines (MARS) using information complexity as the fitness function. Machine Learning 101(1-3):35–58.
  • Mayer, D. G., Kinghorn, B. P., Archer, A. A. (2005). Differential evolution–an easy and efficient evolutionary algorithm for model optimisation. Agricultural Systems 83(3):315–328.
  • Örkcü, H. H. (2013). Subset selection in multiple linear regression models: A hybrid of genetic and simulated annealing algorithms. Applied Mathematics and Computation 219:11018–11028.
  • Pacheco, J., Casado, S., Núñez, L. (2009). A variable selection method based on Tabu search for logistic regression models. European Journal of Operational Research 199(2):506–511.
  • Price, K., Storn, R. M., Lampinen, J. A. (2006). Differential evolution: A practical approach to global optimization. New York: Springer Science & Business Media.
  • Sethanan, K., Pitakaso, R. (2016). Improved differential evolution algorithms for solving generalized assignment problem. Expert Systems with Applications 45:450–459.
  • Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics 6(2):461–464.
  • Storn, R., Price, K. (1997). Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4):341–359.
  • Unler, A., Murat, A. (2010). A discrete particle swarm optimization method for feature selection in binary classification problems. European Journal of Operational Research 206(3):528–539.
  • Wu, T. J., Sepulveda, A. (1998). The weighted average information criterion for order selection in time series and regression models. Statistics & Probability Letters 39(1):1–10.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.