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BioMed Research International
Volume 2015 (2015), Article ID 319797, 11 pages
http://dx.doi.org/10.1155/2015/319797
Research Article

FARMS: A New Algorithm for Variable Selection

1AIDS Research Institute IrsiCaixa-HIVACAT, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona, 08916 Badalona, Spain
2Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
3Institució Catalana de Recerca Avançada (ICREA), 08010 Barcelona, Spain
4University of Vic and Central Catalonia (UVIC-UCC), 08500 Vic, Spain

Received 16 January 2015; Revised 13 March 2015; Accepted 13 March 2015

Academic Editor: Junwen Wang

Copyright © 2015 Susana Perez-Alvarez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. E. Núñez, E. W. Steyerberg, and J. Núñez, “Regression modeling strategies,” Revista Española de Cardiología (English Edition), vol. 64, no. 6, pp. 501–507, 2011. View at Publisher · View at Google Scholar
  2. E. W. Steyerberg, B. van Calster, and M. J. Pencina, “Performance measures for prediction models and markers: evaluation of predictions and classifications,” Revista Espanola de Cardiologia, vol. 64, no. 9, pp. 788–794, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. E. I. George, “The variable selection problem,” Journal of the American Statistical Association, vol. 95, no. 452, pp. 1304–1308, 2000. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  4. R. R. Hocking, “The analysis and selection of variables in linear regression,” Biometrics, vol. 32, no. 1, pp. 1–49, 1976. View at Google Scholar · View at MathSciNet
  5. A. E. Goodenough, A. G. Hart, and R. Stafford, “Regression with empirical variable selection: description of a new method and application to ecological datasets,” PLoS ONE, vol. 7, no. 3, Article ID e34338, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. K. Murray, S. Heritier, and S. M{\"u}ller, “Graphical tools for model selection in generalized linear models,” Statistics in Medicine, vol. 32, no. 25, pp. 4438–4451, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  7. W. Sauerbrei, P. Royston, and H. Binder, “Selection of important variables and determination of functional form for continuous predictors in multivariable model building,” Statistics in Medicine, vol. 26, no. 30, pp. 5512–5528, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. F. G. Blanchet, P. Legendre, and D. Borcard, “Forward selection of explanatory variables,” Ecology, vol. 89, no. 9, pp. 2623–2632, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. R. E. Wiegand, “Performance of using multiple stepwise algorithms for variable selection,” Statistics in Medicine, vol. 29, no. 15, pp. 1647–1659, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  10. M. Hofmann, C. Gatu, and E. J. Kontoghiorghes, “Efficient algorithms for computing the best subset regression models for large-scale problems,” Computational Statistics & Data Analysis, vol. 52, no. 1, pp. 16–29, 2007. View at Publisher · View at Google Scholar · View at Scopus
  11. M. G. G'Sell, T. Hastie, and R. Tibshirani, False Variable Selection Rates in Regression, 2013.
  12. H. S. Rabie and I. W. Saunders, “A simulation study to assess a variable selection method for selecting single nucleotide polymorphisms associated with disease,” Journal of Computational Biology, vol. 19, no. 10, pp. 1151–1161, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. Q. He and D.-Y. Lin, “A variable selection method for genome-wide association studies,” Bioinformatics, vol. 27, no. 1, pp. 1–8, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. B. C. Wallet, D. J. Marchette, J. L. Solka, and E. J. Wegman, “A genetic algorithm for best subset selection in linear regression,” in Proceedings of the 28th Symposium on the Interface, Sydney, Australia, 1996.
  15. A. J. Miller, Subset Selection in Regression, Chapman and Hall/CRC, 2nd edition, 2002. View at Publisher · View at Google Scholar · View at MathSciNet
  16. R. B. O'Hara and M. J. Sillanpää, “A review of Bayesian variable selection methods: what, how and which,” Bayesian Analysis, vol. 4, no. 1, pp. 85–118, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society. Series B: Methodological, vol. 58, no. 1, pp. 267–288, 1996. View at Google Scholar · View at MathSciNet
  18. R. B. Bendel and A. A. Afifi, “Comparison of stopping rules in forward ‘Stepwise’ regression,” Journal of the American Statistical Association, vol. 72, no. 357, pp. 46–54, 1977. View at Publisher · View at Google Scholar
  19. A. Liebminger, L. Seyfang, P. Filzmoser, and K. Varmuza, “A new variable selection method based on all subsets regression,” in Proceedings of the Scandinavian Symposium on Chemometrics, Lappeenranta, Finland, June 2007.
  20. B. Mothe, J. Ibarrondo, A. Llano, and C. Brander, “Virological, immune and host genetics markers in the control of HIV infection,” Disease Markers, vol. 27, no. 3, pp. 105–120, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. N. Frahm, T. Korber, C. M. Adams et al., “Consistent cytotoxic-T-lymphocyte targeting of immunodominant regions in human immunodeficiency virus across multiple ethnicities,” Journal of Virology, vol. 78, no. 5, pp. 2187–2200, 2004. View at Publisher · View at Google Scholar · View at Scopus
  22. R. Draenert, M. Altfeld, C. Brander et al., “Comparison of overlapping peptide sets for detection of antiviral CD8 and CD4 T cell responses,” Journal of Immunological Methods, vol. 275, no. 1-2, pp. 19–29, 2003. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Wicker, E. Schlagenhauf, A. Graner, T. J. Close, B. Keller, and N. Stein, “454 sequencing put to the test using the complex genome of barley,” BMC Genomics, vol. 7, article 275, 2006. View at Publisher · View at Google Scholar · View at Scopus
  24. J. G. Prado, J. Carrillo, J. Blanco-Heredia, and C. Brander, “Immune correlates of HIV control,” Current Medicinal Chemistry, vol. 18, no. 26, pp. 3963–3970, 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. B. Mothe, A. Llano, J. Ibarrondo et al., “Definition of the viral targets of protective HIV-1-specific T cell responses,” Journal of Translational Medicine, vol. 9, article 208, 2011. View at Publisher · View at Google Scholar
  26. C. L. Mallows, “Some comments on Cp,” Technometrics, vol. 15, no. 4, pp. 661–675, 1973. View at Google Scholar
  27. H. Akaike, “Information theory and an extension of the maxiumum likelihood principle,” in Proceedings of the 2nd International Symposium on Information Theory, Budapest, Hungary, 1973.
  28. G. Schwarz, “Estimating the dimension of a model,” The Annals of Statistics, vol. 6, no. 2, pp. 461–464, 1978. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  29. E. Gayawan and R. A. Ipinyomi, “A comparison of Akaike, Schwarz and R square criteria for model selection using some fertility models,” Australian Journal of Basic and Applied Sciences, vol. 3, no. 4, pp. 3524–3530, 2009. View at Google Scholar · View at Scopus
  30. J. Kuha, “AIC and BIC: comparisons of assumptions and performance,” Sociological Methods and Research, vol. 33, no. 2, pp. 188–229, 2004. View at Publisher · View at Google Scholar · View at Scopus