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All issues Volume 19 (2015) ESAIM: PS, 19 (2015) 725-745 References
Free Access
Issue
ESAIM: PS
Volume 19, 2015
Page(s) 725 - 745
DOI https://doi.org/10.1051/ps/2015013
Published online 11 December 2015
  1. P.J. Bickel, Y. Ritov and A.B. Tsybakov, Simultaneous analysis of Lasso and Dantzig selector. Ann. Stat. 37 (2009) 1705–1732. [Google Scholar]
  2. P.J. Bickel, Y. Ritov and A.B. Tsybakov, Simultaneous analysis of lasso and Dantzig selector. Ann. Stat. 37 (2009) 1705–1732. [Google Scholar]
  3. P.L. Bühlmann and S.A. van de Geer, Statistics for High-Dimensional Data. Springer (2011). [Google Scholar]
  4. E.J. Candès and Y. Plan, Near-ideal model selection by L1 minimization. Ann. Stat. 37 (2009) 2145–2177. [CrossRef] [Google Scholar]
  5. E.J. Candes and T. Tao, The Dantzig selector: statistical estimation when p is much larger than n. Ann. Stat. 35 (2007) 2313–2351. [Google Scholar]
  6. E.J. Candes, J.K. Romberg and T. Tao, Stable signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59 (2006) 1207–1223. [CrossRef] [MathSciNet] [Google Scholar]
  7. D. Chafaı, O. Guédon, G. Lecué and A. Pajor, Interactions Between Compressed Sensing, Random Matrices, and High Dimensional Geometry. Panoramas et Synthèses. SMF (2012). [Google Scholar]
  8. Y. de Castro, A remark on the lasso and the Dantzig selector. Stat. Probab. Lett. (2012). [Google Scholar]
  9. D.L. Donoho, M. Elad and V.N. Temlyakov, Stable recovery of sparse overcomplete representations in the presence of noise. Inf. Theory IEEE Trans. 52 (2006) 6–18. [Google Scholar]
  10. B. Efron, T. Hastie, I. Johnstone and R.J. Tibshirani, Least angle regression. Ann. Stat. 32 (2004) 407–499. [CrossRef] [MathSciNet] [Google Scholar]
  11. Jianqing Fan, Yang Feng and Rui Song, Nonparametric independence screening in sparse ultra-high-dimensional additive models. J. Am. Stat. Assoc. 106 (2011). [Google Scholar]
  12. J.-J. Fuchs, On sparse representations in arbitrary redundant bases. Inf. Theory IEEE Trans. 50 (2004) 1341–1344. [Google Scholar]
  13. F. Gamboa, A. Janon, T. Klein, A. Lagnoux-Renaudie and C. Prieur, Statistical inference for Sobol pick freeze Monte Carlo method. Preprint arXiv:1303.6447 (2013). [Google Scholar]
  14. R. Gray, Toeplitz and Circulant Matrices: A Review. Now Publishers Inc. (2006). [Google Scholar]
  15. W. Hoeffding, Probability Inequalities for Sums of Bounded Random Variables. J. Am. Statist. Assoc. 58 (1963) 13–30. [Google Scholar]
  16. A. Janon, T. Klein, A. Lagnoux, M. Nodet and C. Prieur, Asymptotic normality and efficiency of two Sobol index estimators. Preprint available at http://hal.inria.fr/hal-00665048/en (2012). [Google Scholar]
  17. A. Juditsky and A. Nemirovski, Accuracy Guarantees for-Recovery. Inf. Theory IEEE Trans. 57 (2011) 7818–7839. [CrossRef] [Google Scholar]
  18. R. Liu and A.B. Owen, Estimating Mean Dimensionality. Department of Statistics, Stanford University (2003). [Google Scholar]
  19. K. Lounici, Sup-norm convergence rate and sign concentration property of Lasso and Dantzig estimators. Electron. J. Stat. 2 (2008) 90–102. [CrossRef] [Google Scholar]
  20. H. Monod, C. Naud and D. Makowski, Uncertainty and sensitivity analysis for crop models. In Chap. 4. Working with Dynamic Crop Models: Evaluation, Analysis, Parameterization, and Applications. Edited by D. Wallach, D. Makowski and J.W. Jones. Elsevier (2006) 55–99. [Google Scholar]
  21. M.D. Morris, Factorial sampling plans for preliminary computational experiments. Technometrics 33 (1991) 161–174. [Google Scholar]
  22. A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana and S. Tarantola, Global Sensitivity Analysis: The Primer. Wiley Online Library (2008). [Google Scholar]
  23. I.M. Sobol, Sensitivity estimates for nonlinear mathematical models. Math. Model. Comput. Experiment 1 (1993) 407–414. [Google Scholar]
  24. I.M. Sobol, Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates. Math. Comput. Simul. 55 (2001) 271–280. [Google Scholar]
  25. S. Tarantola, et al., Estimating the approximation error when fixing unessential factors in global sensitivity analysis. Reliab. Eng. Syst. Safety 92 (2007) 957–960. [CrossRef] [Google Scholar]
  26. R.J. Tibshirani, Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B. Methodological (1996) 267–288. [Google Scholar]
  27. J.-Y. Tissot and C. Prieur, Bias correction for the estimation of sensitivity indices based on random balance designs. Reliab. Eng. Syst. Safety 107 (2012) 205–213. [Google Scholar]
  28. J.-Y. Tissot and C. Prieur, Estimating Sobol’Indices Combining Monte Carlo Estimators and Latin Hypercube Sampling (2012). [Google Scholar]
  29. J.A. Tropp, Just relax: Convex programming methods for identifying sparse signals in noise. Inf. Theory IEEE Trans. 52 (2006) 1030–1051. [CrossRef] [MathSciNet] [Google Scholar]
  30. S.A. van de Geer, and P. Bühlmann, On the conditions used to prove oracle results for the Lasso. Electron. J. Stat. 3 (2009) 1360–1392. [Google Scholar]
  31. L. Welch, Lower bounds on the maximum cross correlation of signals (Corresp.). Inf. Theory, IEEE Trans. 20 (1974) 397–399. [CrossRef] [Google Scholar]
  32. P. Zhao and B. Yu, On model selection consistency of Lasso. J. Mach. Learn. Res. 7 (2006) 2541–2563. [Google Scholar]

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