Stochastic Stepwise Ensembles for Variable Selection

References

• Akaike, H. (1973), “Information Theory and an Extension of the Maximum Likelihood Principle,” in Second International Symposium on Information Theory, pp. 267–281.
   Google Scholar
• Breiman, L. (1995), “Better Subset Regression Using the Nonnegative Garrote,” Technometrics, 37, 373–384.
   Web of Science ®Google Scholar
• ——— (1996), “Bagging Predictors,” Machine Learning, 24(2), 123–140.
   Web of Science ®Google Scholar
• ——— (2001), “Random Forests,” Machine Learning, 45(1), 5–32.
   Web of Science ®Google Scholar
• Donoho, D.L. (1995), “De-noising by Soft-Thresholding,” IEEE Transactions on Information Theory, 41(3), 613–627.
   Web of Science ®Google Scholar
• Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. (2004), “Least Angle Regression” (with discussion), The Annals of Statistics, 32(2), 407–499.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconcave Penalized Likelihood and Its Oracle Properties,” Journal of the American Statistical Association, 96(456), 1348–1360.
   Web of Science ®Google Scholar
• Fan, J., and Lv, J. (2008), “Sure Independence Screening for Ultrahigh Dimensional Feature Space” (with discussion), Journal of the Royal Statistical Society, Series B, 70, 849–911.
   Google Scholar
• Freund, Y., and Schapire, R. (1996), “Experiments With a New Boosting Algorithm,” in Machine Learning: Proceedings of the 13th International Conference, pp. 148–156.
   Google Scholar
• Friedman, J. H. (2001), “Greedy Function Approximation: A Gradient Boosting Machine,” The Annals of Statistics, 29(5), 1189–1232.
   Web of Science ®Google Scholar
• Friedman, J. H., Hastie, T. J., and Tibshirani, R. J. (2000), “Additive Logistic Regression: A Statistical View of Boosting” (with discussion), The Annals of Statistics, 28(2), 337–407.
   Web of Science ®Google Scholar
• Friedman, J. H., and Popescu, B. (2003), “Importance-Sampled Learning Ensembles,” Technical Report, Stanford University.
   Google Scholar
• Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Reading, MA: Addison-Wesley.
   Google Scholar
• Ho, T. K. (1998), “The Random Subspace Method for Constructing Decision Forests,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8), 832–844.
   Web of Science ®Google Scholar
• Jolliffe, I. T. (2002), Principal Component Analysis (2nd ed.), New York: Springer-Verlag.
   Google Scholar
• Liu, J. S. (2001), Monte Carlo Strategies in Scientific Computing, New York: Springer-Verlag.
   Google Scholar
• Meinshausen, N. (2007), “Relaxed Lasso,” Computational Statistics & Data Analysis, 52, 374–393.
   Web of Science ®Google Scholar
• Meinshausen, N., and Bühlmann, P. (2010), “Stability Selection” (with discussion), Journal of the Royal Statistical Society, Series B, 72, 417–473.
   Google Scholar
• Miller, A. J. (2002), Subset Selection in Regression (2nd ed.), Boca Raton, FL: Chapman & Hall.
   Google Scholar
• Radchenko, P., and James, G. (2008), “Variable Inclusion and Shrinkage Algorithms,” Journal of the American Statistical Association, 103, 1304–1315.
   Web of Science ®Google Scholar
• Shmueli, G. (2010), “To Explain or to Predict?” Statistical Science, 25(3), 289–310.
   Web of Science ®Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
   Google Scholar
• Wang, S., Nan, B., Rosset, S., and Zhu, J. (2011), “Random Lasso,” The Annals of Applied Statistics, 5(1), 468–485.
   PubMed Web of Science ®Google Scholar
• Yang, Y. (2005), “Can the Strengths of AIC and BIC Be Shared? A Conflict Between Model Indentification and Regression Estimation,” Biometrika, 92, 937–950.
   Web of Science ®Google Scholar
• Zhu, M. (2008), “Kernels and Ensembles: Perspectives on Statistical Learning,” The American Statistician, 62, 97–109.
   Web of Science ®Google Scholar
• Zhu, M., and Chipman, H. A. (2006), “Darwinian Evolution in Parallel Universes: A Parallel Genetic Algorithm for Variable Selection,” Technometrics, 48, 491–502.
   Web of Science ®Google Scholar
• Zou, H. (2006), “The Adaptive Lasso and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
   Web of Science ®Google Scholar
• Zou, H., and Hastie, T. J. (2005), “Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320.
   Google Scholar