29 references, last updated Wed Apr 18 16:13:22 2012

• C Ambroise and GJ McLachlan. Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences, 99(10):6562–6566, 2002.
• T Bo and I Jonassen. New feature subset selection procedures for classification of expression profiles. Genome Biology, 3(4):0017.1–0017.11, 2002.
• L. Breiman. Bagging predictors. Machine Learning, 26:123–140, 1996. (PDF, 197687 bytes)
• L Breiman. Random forests. Machine Learning, 45(1):5–32, 2001. (PDF, 123819 bytes)
• L Breiman. Statistical modeling: The two cultures. Statistical Science, 16(3):199–231, 2001.
• F Bunea, Y She, H Ombao, A Gongvatana, K Devlin, and Cohen R. Penalized least squares regression methods and applications to neuroimaging. NeuroImage, 55:1519–1527, 2011. (PDF, 349432 bytes)
• E Candes and T Tao. The Dantzig selector: statistical estimation when p is much larger than n. The Annals of Statistics, 35(6):2312–2351, 2007. (PDF)
• R. Díaz-Uriarte and S. Alvarez de Andrés. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7(3), 2006.
• J Fan and R Li. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association, 96:1348–1360, 2001. (PDF, 399298 bytes)
• JH Friedman, T Hastie, and R Tibsharini. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1), 2010.
• JH Friedman. Fast sparse regression and classification. In In Proceedings of the 23rd International Workshop on Statistical Modelling, pages 27–57, 2008. (PDF)
• WJ Fu. Penalized regressions: The bridge versus the Lasso. Journal of Computational and Graphical Statistics, 7(3):397–416, 1998. (PDF, 194601 bytes)
• I Guyon, S Gunn, M Nikravesh, and L A Zadeh, editors. Feature Extraction: Foundations And Applications. Springer-Verlag, 2006.
• FE Harrell. Regression Modeling Strategies. Springer, 2001.
• T Hastie, R Tibshirani, and J Friedman. The Elements of Statistical Learning. Springer, 2ème edition, 2009.
• AE Hoerl and RW Kennard. Ridge regression: Applications to nonorthogonal problems. Technometrics, 12(1):69–82, 1970. (PDF, 619213 bytes)
• T Hothorn, F Leisch, A Zeilis, and K Hornik. The design and analysis of benchmark experiments. Journal of Computational and Graphical Statistics, 14(3):675–699, 2005. (PDF, 589534 bytes)
• M Jelizarow, V Guillemot, A Tenenhaus, K Strimmer, and A-L Boulesteix. Over-optimism in bioinformatics: an illustration. Bioinformatics, 26(16):1990–1998, 2010.
• M Kyung, J Gill, M Ghosh, and G Casella. Penalized regression, standard errors, and bayesian lassos. Bayesian Analysis, 5(2):369–412, 2010. (PDF, 511582 bytes)
• T N Lal, O Chapelle, J Weston, and A Elisseeff. Embedded methods. In I Guyon, S Gunn, M Nikravesh, and L A Zadeh, editors, Feature Extraction: Foundations And Applications, pages 137–162. Springer-Verlag, 2006.
• A Long, H Mangalam, B Chan, L Tolleri, G Hatfield, and P Baldi. Improved statistical inference from dna microarray data using analysis of variance and a Bayesian statistical framework. Journal of Biological Chemistry, 276:19937–19944, 2001.
• A M Molinaro, R Simon, and R M Pfeiffer. Prediction error estimation: A comparison of resampling methods. Bioinformatics, 21(15):3301–3307, 2005.
• AY Ng and MI Jordan. On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes. Neural Information Processing Systems, pages 841–848, 2001. (PDF, 188721 bytes)
• D Paul, E Bair, T Hastie, and R Tibshirani. Pre-conditioning for feature selection and regression in high-dimensional problems. The Annals of Statistics, 36:1595–1618, 2008. (PDF, 985163 bytes)
• H Schwender, K Ickstadt, and Rahnenführer. Classification with high-dimensional genetic data: Assigning patients and genetic features to known classes. Biometrical Journal, 6:911–926, 2008. (PDF, 168927 bytes)
• G Seni and J Elder. Ensemble Methods in Data Mining. Morgan & Claypool, 2010.
• R Tibshirani. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B, 58:267–288, 1996. (PDF, 2030051 bytes)
• S Varma and R Simon. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics, 7(91), 2006.
• H Zou and T Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society, Series B, 67(2):301–320, 2005. (PDF)