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Institute of Mathematical Statistics
Electronic Journal of Statistics (108)
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She, Y. (2009). Thresholding-based iterative selection procedures for model selection and shrinkage. Electronic Journal of Statistics, 3(none).

Authors 1
  1. Yiyuan She (first)
References 40 Referenced 96
  1. [1] Antoniadis, A. Wavelets in statistics: a review (with discussion)., <i>Italian Journal of Statistics 6 </i> (1997), 97–144. (10.1007/BF03178905)
  2. [2] Antoniadis, A. Wavelet methods in statistics: Some recent developments and their applications., <i>Statistics Surveys 1 </i> (2007), 16–55. (10.1214/07-SS014)
  3. [3] Antoniadis, A., and Fan, J. Regularization of wavelets approximations., <i>JASA 96 </i> (2001), 939–967. (10.1198/016214501753208942)
  4. [4] Browder, F.E., and Petryshyn, W.V. Construction of fixed points of nonlinear mappings in Hilbert space., <i>Journal of Mathematical Analysis and Applications 20</i>, 2 (1967), 197–228. (10.1016/0022-247X(67)90085-6)
  5. [5] Bunea, F., Tsybakov, A.B., and Wegkamp, M. Sparsity oracle inequalities for the lasso., <i>Electronic Journal of Statistics 1 </i> (2007), 169–194. (10.1214/07-EJS008)
  6. [6] Cai, J., Fan, J., Zhou, H., and Zhou, Y. Hazard models with varying coefficients for multivariate failure time data., <i>Annals of Statistics 35 </i> (2007), 324. (10.1214/009053606000001145)
  7. [7] Candès, E. Modern statistical estimation via oracle inequalities., <i>Acta Numerica 15 </i> (2006), 257–325. (10.1017/S0962492906230010)
  8. [8] Candès, E., Romberg, J., and Tao, T. Stable signal recovery from incomplete and inaccurate measurements., <i>Comm. Pure Appl. Math. 59 </i> (2006), 1207–1223. (10.1002/cpa.20124)
  9. [9] Candès, E., and Tao, T. The Dantzig selector: statistical estimation when p is much smaller than n., <i>Annals of Statistics 35 </i> (2005), 2392–2404. (10.1214/009053607000000532)
  10. [10] Daubechies, I., Defrise, M., and De Mol, C. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint., <i>Communications on Pure and Applied Mathematics 57 </i> (2004), 1413–1457. (10.1002/cpa.20042)
  11. [11] Donoho, D., Elad, M., and Temlyakov, V. Stable recovery of sparse overcomplete representations in the presence of noise., <i>IEEE Transactions on Information Theory 52 </i> (2006), 6–18. (10.1109/TIT.2005.860430)
  12. [12] Donoho, D., and Johnstone, I. Ideal spatial adaptation via wavelet shrinkages., <i>Biometrika 81 </i> (1994), 425–455. (10.1093/biomet/81.3.425)
  13. [13] Efron, B., Hastie, T., Johnstone, I., and Tibshirani, R. Least angle regression., <i>Annals of Statistics 32 </i> (2004), 407–499. (10.1214/009053604000000067)
  14. [14] Fan, J. Comment on ‘Wavelets in Statistics: A Review’ by A. Antoniadis., <i>Italian Journal of Statistics 6 </i> (1997), 97–144. (10.1007/BF03178905)
  15. [15] Fan, J., and Li, R. Variable selection via nonconcave penalized likelihood and its oracle properties., <i>J. Amer. Statist. Assoc. 96 </i> (2001), 1348–1360. (10.1198/016214501753382273)
  16. [16] Friedman, J., Hastie, T., Hofling, H., and Tibshirani, R. Pathwise coordinate optimization., <i>Annals of Applied Statistics 1 </i> (2007), 302. (10.1214/07-AOAS131)
  17. [17] Fu, W. Penalized regressions: the bridge vs the lasso., <i>JCGS 7</i>, 3 (1998), 397–416. (10.1080/10618600.1998.10474784)
  18. [18] Gannaz, I. Robust estimation and wavelet thresholding in partial linear models. Tech. rep., University Joseph Fourier, Grenoble, France, 2006.
  19. [19] Gao, H.-Y. Wavelet shrinkage denoising using the non-negative garrote., <i>J. Comput. Graph. Statist. 7 </i> (1998), 469–488. (10.1080/10618600.1998.10474789)
  20. [20] Geman, D., and Reynolds, G. Constrained restoration and the recovery of discontinuities., <i>IEEE PAMI 14</i>, 3 (1992), 367–383. (10.1109/34.120331)
  21. [21] Hunter, D.R., and Lange, K. Rejoinder to discussion of ‘Optimization transfer using surrogate objective functions’., <i>J. Comput. Graphical Stat 9 </i> (2000), 52–59. (10.2307/1390612)
  22. [22] Hunter, D.R., and Li, R. Variable selection using mm algorithms., <i>Annals of Statistics 33 </i> (2005), 1617–1642. (10.1214/009053605000000200)
  23. [23] Knight, K., and Fu, W. Asymptotics for lasso-type estimators., <i>Annals of Statistics 28 </i> (2000), 1356–1378. (10.1214/aos/1015957397)
  24. [24] Meinshausen, N. Relaxed lasso., <i>Computational Statistics and Data Analysis 52</i>, 1 (2007), 374–393. (10.1016/j.csda.2006.12.019)
  25. [25] Meinshausen, N., and Yu, B. Lasso-type recovery of sparse representations for high-dimensional data., <i>Annals of Statistics</i>, 720 (2009), 246–270. (10.1214/07-AOS582)
  26. [26] Osborne, M., Presnell, B., and Turlach, B. On the LASSO and its dual., <i>J. Comput. Graph. Statist. 9</i>, 2 (2000), 319–337. (10.1080/10618600.2000.10474883)
  27. [27] She, Y., <i>Sparse Regression with Exact Clustering</i>. PhD thesis, Stanford University, 2008. (10.1214/10-EJS578)
  28. [28] She, Y. Thresholding-based iterative selection procedures for model selection and shrinkage. Tech. rep., Statistics Department, Stanford University, June, 2008. (10.1214/08-EJS348)
  29. [29] Shimizu, K., Ishizuka, Y., and Bard, J., <i>Nondifferentiable and Two-Level Mathematical Programming</i>. Kluwer Academic Publishers, 1997. (10.1007/978-1-4615-6305-1)
  30. [30] Tibshirani, R. Regression shrinkage and selection via the lasso., <i>JRSSB 58 </i> (1996), 267–288. (10.1111/j.2517-6161.1996.tb02080.x)
  31. [31] Šidák, Z. Rectangular confidence regions for the means of multivariate normal distribution., <i>JASA 62 </i> (1967), 626–633. (10.1080/01621459.1967.10482935)
  32. [32] Wang, L., Chen, G., and Li, H. Group scad regression analysis for microarray time course gene expression data., <i>Bioinformatics 23</i>, 12 (2007), 1486–1494. (10.1093/bioinformatics/btm125)
  33. [33] Wu, T., and Lange, K. Coordinate descent algorithm for lasso penalized regression., <i>Ann. Appl. Stat. 2</i>, 1 (2008), 224–244. (10.1214/07-AOAS147)
  34. [34] Yuan, M., and Lin, Y. Model selection and estimation in regression with grouped variables., <i>JRSSB 68 </i> (2006), 49–67. (10.1111/j.1467-9868.2005.00532.x)
  35. [35] Zhang, C.-H., and Huang, J. The sparsity and bias of the Lasso selection in high-dimensional linear regression., <i>Ann. Statist 36 </i> (2008), 1567–1594. (10.1214/07-AOS520)
  36. [36] Zhang, H.H., Ahn, J., Lin, X., and Park, C. Gene selection using support vector machines with non-convex penalty., <i>Bioinformatics 22</i>, 1 (2006), 88–95. (10.1093/bioinformatics/bti736)
  37. [37] Zhao, P., and Yu, B. On model selection consistency of lasso., <i>Journal of Machine Learning Research 7 </i> (2006), 2541–2563.
  38. [38] Zou, H. The adaptive lasso and its oracle properties., <i>JASA 101</i>, 476 (2006), 1418–1429. (10.1198/016214506000000735)
  39. [39] Zou, H., and Hastie, T. Regularization and variable selection via the elastic net., <i>JRSSB 67</i>, 2 (2005), 301–320. (10.1111/j.1467-9868.2005.00503.x)
  40. [40] Zou, H., and Li, R. One-step sparse estimates in nonconcave penalized likelihood models., <i>Annals of Statistics </i> (2008), 1509–1533. (10.1214/009053607000000802)
Dates
Type When
Created 16 years, 4 months ago (April 27, 2009, 7:31 a.m.)
Deposited 4 years, 3 months ago (May 9, 2021, 7:11 a.m.)
Indexed 3 months ago (May 27, 2025, 11:02 a.m.)
Issued 16 years, 8 months ago (Jan. 1, 2009)
Published 16 years, 8 months ago (Jan. 1, 2009)
Published Print 16 years, 8 months ago (Jan. 1, 2009)
Funders 0

None

@article{She_2009, title={Thresholding-based iterative selection procedures for model selection and shrinkage}, volume={3}, ISSN={1935-7524}, url={http://dx.doi.org/10.1214/08-ejs348}, DOI={10.1214/08-ejs348}, number={none}, journal={Electronic Journal of Statistics}, publisher={Institute of Mathematical Statistics}, author={She, Yiyuan}, year={2009}, month=jan }