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Combining thresholding rules: a new way to improve the performance of wavelet estimators

  1. Abramovich Felix, Benjamini Yoav, Donoho David L., Johnstone Iain M., Adapting to unknown sparsity by controlling the false discovery rate, 10.1214/009053606000000074
  2. Antoniadis Anestis, Bigot Jérémie, Sapatinas Theofanis, Wavelet Estimators in Nonparametric Regression: A Comparative Simulation Study, 10.18637/jss.v006.i06
  3. Autin, F. (2004), ‘Maxiset Point of View in Nonparametric Estimation’, Ph.D. thesis, Université Paris 7 - Denis Diderot.
  4. Autin Florent, On the performances of a new thresholding procedure using tree structure, 10.1214/08-ejs205
  5. Autin Florent, Maxisets for μ-thresholding rules, 10.1007/s11749-006-0035-5
  6. Autin F., Mathematical Methods of Statistics, 15, 349 (2006)
  7. Autin F., Le Pennec E., Loubes J. M., Rivoirard V., Maxisets for Model Selection, 10.1007/s00365-009-9062-2
  8. Autin Florent, Freyermuth Jean-Marc, von Sachs Rainer, Ideal denoising within a family of tree-structured wavelet estimators, 10.1214/11-ejs628
  9. Autin, F., Freyermuth, J.M., and von Sachs, R. (2011b), ‘Block-Threshold-Adapted Estimators via a Maxiset Approach’. Preprint 2011/17, ISBA, Université Catholique de Louvain.
  10. Barber Stuart, Nason Guy P., Real nonparametric regression using complex wavelets, 10.1111/j.1467-9868.2004.b5604.x
  11. Cai, T. (1997), ‘On Adaptivity of Blockshrink Wavelet Estimator over Besov Spaces’, Technical Report 97-05, Purdue University.
  12. Cai T. Tony, inequality approach, 10.1214/aos/1018031262
  13. Cai T. Tony, On information pooling, adaptability and superefficiency in nonparametric function estimation, 10.1016/j.jmva.2006.11.010
  14. Cai T., Sankhya, 63, 127 (2001)
  15. Cai T. Tony, Zhou Harrison H., A data-driven block thresholding approach to wavelet estimation, 10.1214/07-aos538
  16. Cohen Albert, Dahmen Wolfgang, Daubechies Ingrid, DeVore Ronald, Tree Approximation and Optimal Encoding, 10.1006/acha.2001.0336
  17. Cohen Albert, DeVore Ronald, Kerkyacharian Gerard, Picard Dominique, Maximal Spaces with Given Rate of Convergence for Thresholding Algorithms, 10.1006/acha.2000.0333
  18. Daubechies Ingrid, Ten Lectures on Wavelets, ISBN:9780898712742, 10.1137/1.9781611970104
  19. Engel J., A Simple Wavelet Approach to Nonparametric Regression from Recursive Partitioning Schemes, 10.1006/jmva.1994.1024
  20. Fryzlewicz P., Statistica Sinica, 17, 1457 (2007)
  21. Gordon Robert D., Values of Mills' Ratio of Area to Bounding Ordinate and of the Normal Probability Integral for Large Values of the Argument, 10.1214/aoms/1177731721
  22. Hall Peter, Kerkyacharian Gérard, Picard Dominique, Block threshold rules for curve estimation using kernel and wavelet methods, 10.1214/aos/1024691082
  23. Hall P., Statistica Sinica, 9, 33 (1998)
  24. Kerkyacharian Gérard, Picard Dominique, Birgé Lucien, Hall Peter, Lepski Oleg, Mammen Enno, Tsybakov Alexandre, Kerkyacharian G., Picard D., Thresholding algorithms, maxisets and well-concentrated bases, 10.1007/bf02595738
  25. Kerkyacharian G., Bernoulli, 8, 219 (2002)
  26. Kohn R., Statistica Sinica, 10, 109 (2000)
  27. Lepskii O. V., Asymptotically Minimax Adaptive Estimation. I: Upper Bounds. Optimally Adaptive Estimates, 10.1137/1136085
  28. Statistical Modeling by Wavelets, ISBN:9780470317020, 10.1002/9780470317020
Bibliographic reference Autin, Florent ; Freyermuth, Jean-Marc ; von Sachs, Rainer. Combining thresholding rules: a new way to improve the performance of wavelet estimators. In: Journal of Nonparametric Statistics, Vol. 24, no. 4, p. 905-922 (2012)
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