Free Access
Issue
Math. Model. Nat. Phenom.
Volume 5, Number 3, 2010
Mathematical modeling in the medical sciences
Page(s) 115 - 133
DOI https://doi.org/10.1051/mmnp/20105308
Published online 28 April 2010
  1. L. Breiman. Better subset regression using the nonnegative garrote Technometrics, 37 (1995), 373-384. [CrossRef] [MathSciNet] [Google Scholar]
  2. P. Chaurand, M.E. Sanders, R.A. Jensen, R.M. Caprioli. Profiling and imaging proteins in tissue sections by MS. Anal. Chem., 76 (2004), 86A-93A. [CrossRef] [Google Scholar]
  3. G. Chu, B. Narasimhan, R. Tibshirani, V.G. Tusher. SAM Version 1.12: user’s guide and technical document.[http://www-stat.stanford.edu/ tibs/SAM/] [Google Scholar]
  4. E. Candes, T. Tao. The dantzig selector: statistical estimation when p is much larger than n. Annals of Statistics, 35 (2007), 2313. [CrossRef] [MathSciNet] [Google Scholar]
  5. B. Efron, T. Hastie, R. Tibshirani. Least angle regression. Annals of Statistics, 32 (2004), 407-499. [CrossRef] [MathSciNet] [Google Scholar]
  6. J. Fan, R. Li. Variable selection via nonconcave penalized Likelihood and Its Oracle Properties. Journal of the American Statistical Association, 96 (2001), 1348-1360. [CrossRef] [MathSciNet] [Google Scholar]
  7. I. Frank, J. Friedman. A statistical view of some chemometrics regression tools. Technometrics, 35 (1993), 109-148. [CrossRef] [Google Scholar]
  8. M. Gerhard, S.O. Deininger, F.M. Schleif. Statistical Classification and visualization of MALDI imaging data. CBMS’07 2007; 0-7695-2905-4/07. [Google Scholar]
  9. D.J. Graham, M.S. Wagner, D.G. Castner. Information from complexity: challenges of TOF-SIMS data interpretation. Applied surface science, 252 (2006), 6860-6868. [CrossRef] [Google Scholar]
  10. P. Hall, J.S. Marron, A. Neeman. Geometric representation of high dimension low sample size data. J. R. Statist. Soc. B, 67 (2005), 427. [CrossRef] [Google Scholar]
  11. T. Hastie, R. Tibshirani, J. Friedman. The elements of statistical learning; Data mining, inference and prediction. Springer, New York, 2001. [Google Scholar]
  12. A. E. Hoerl, R. W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12 (1970), 55-67. [CrossRef] [Google Scholar]
  13. J. Huang, J. Horowitz, S. Ma. Asymptotic properties of bridge estimators in sparse high-dimensional regression models. Annals Statatistics, 36 (2008), 587-613. [CrossRef] [Google Scholar]
  14. J. Huang, S. Ma, C. Zhang. Adaptive Lasso for sparse high dimensional regression models. Stat Sin, 18 (2008), 1603-1618. [Google Scholar]
  15. G.M. James, P. Radchenko, and J. Lv. DASSO: connections between the Dantzig selector and lasso. J. R. Statist. Soc. B, 71 (2009) pp. 127(C142. [CrossRef] [Google Scholar]
  16. J. Jia, B. Yu. On model selection consistency of the elastic net when pn. Tech. Report 756, Statistics, UC Berkeley, 2008. [Google Scholar]
  17. K. Knight, W. Fu. Asymptotics for Lasso-type estimators. Annals Statistics, 28 (2000), 1356-1378. [CrossRef] [MathSciNet] [Google Scholar]
  18. S. Matoba, J.G. Kang, W.D. Patino, A. Wragg, M. Boehm, O. Gavrilova, P.J. Hurley, F. Bunz, P.M. Hwang. P53 regulates mitochondrial respiration. Science, 312 (2006), 1650-1653. [CrossRef] [PubMed] [Google Scholar]
  19. S. Ma, J. Huang. Penalized feature selection and classification in bioinformatics. Brief in Bioinform., 9 (2008), 392-403. [CrossRef] [Google Scholar]
  20. A. Mayevsky. Mitochondrial function and energy metabolism in cancer cells: Past overview and future perspectives. Mitochondrion, 9 (2009), 165-179. [CrossRef] [PubMed] [Google Scholar]
  21. G. McCombie, D. Staab, M. Stoeckli, R. Knochenmuss. Spatial and Spectral correlation in MALDI mass spectrometry images by clustering and multivariate analysis. Anal. Chem. 2005;77:6118-6124. [CrossRef] [PubMed] [Google Scholar]
  22. N. Meinshausen, B. Yu. Lasso-type recovery of sparse representations for high-dimensional data. Annals of Statistics, 37 (2009), no. 1, 246-270. [CrossRef] [MathSciNet] [Google Scholar]
  23. H. Meistermann, J.L. Norris, H.R. Aerni, D.S. Cornett, A. Friedlein, A.R. Erskine, A. Augustin, M.C. De Vera Mudry, S. Ruepp, L. Suter, H. Langen, R.M. Caprioli, A. Ducret. Biomarker discovery by imaging mass spectrometry: transthyretin is a biomarker for gentamicin-induced nephrotoxicity in rat. Mol Cell Proteomics, 5 (2006), 1876-1886. [CrossRef] [PubMed] [Google Scholar]
  24. E.R. Muir, I.J. Ndiour, N.A. Le Goasduff, R.A. Moffitt, Y. Liu, M.C. Sullards, A.H. Merrill, Y. Chen, M.D. Wang. Multivariate analysis of imaging mass spectrometry data. BIBE 2007 proceedings of the 7th IEEE international conference 472-479. [Google Scholar]
  25. R. Tibshirani. Regression shrinkage and selection via the lasso. J. R. Statist. Soc., Series B., 58(1), 1996, 267-288. [Google Scholar]
  26. M. Yuan, Y. Lin. On the nonnegative garrote estimator. J. R. Statist. Soc. B., 69 (2007), 143-161. [CrossRef] [Google Scholar]
  27. F. Zhang, D. Hong, S. Frappier, D.S. Cornett, R.M. Caprioli. Elastic Net Based Framework for Imaging Mass Spectrometry Data Biomarker Selection and Classification. Manuscript, 2009. [Google Scholar]
  28. H. Zhang, J. Ahn, X. Lin, C. Park. Gene selection using support vector machines with non-convex penalty. Bioinformatics, 22 (2006), 88-95. [CrossRef] [PubMed] [Google Scholar]
  29. P. Zhao, B. Yu. On model selection consistency of lasso. The Journal of Machine Learning Research, 7 (2006), 2541-2563. [Google Scholar]
  30. S. Zhou, S. Geer, P. Buhlmann. Adaptive lasso for high dimensional regression and gaussian graphical modeling. manuscript, 2009. [Google Scholar]
  31. H. Zou. The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101 (2006), 1418-1429. [CrossRef] [MathSciNet] [Google Scholar]
  32. H. Zou, T. Hastie. Regularization and variable selection via the elastic net. J. R. Statist. Soc., B. 67(2005), Part 2, 301-320. [CrossRef] [Google Scholar]
  33. H. Zou, H. Zhang. On the adaptive elastic-net with a diverging number of parameters. Annals of statistics, 37 (2009), 1733-1751. [CrossRef] [MathSciNet] [PubMed] [Google Scholar]

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