EDP Sciences logo
Subscriber Authentication Point
Search
Menu
All issues Volume 9 (June 2005) ESAIM: PS, 9 (2005) 143-164 References
Free Access
Issue
ESAIM: PS
Volume 9, June 2005
Page(s) 143 - 164
DOI https://doi.org/10.1051/ps:2005007
Published online 15 November 2005
  1. A. Antoniadis, J. Bigot and T. Sapatinas, Wavelet estimators in nonparametric regression: a comparative simulation study. J. Statist. Software 6 (2001) 1–83. [Google Scholar]
  2. A. Antoniadis and I. Gijbels, Detecting abrupt changes by wavelet methods. J. Nonparam. Statist 14 (2001) 7–29. [CrossRef] [Google Scholar]
  3. A. Arneodo, E. Bacry, S. Jaffard and J.F. Muzy, Oscillating singularities and fractal functions, in Spline functions and the theory of wavelets (Montreal, PQ, 1996), Amer. Math. Soc., Providence, RI. CRM Proc. Lect. Notes 18 (1999) 315–329.. [Google Scholar]
  4. A. Arneodo, E. Bacry, S. Jaffard and J.F. Muzy, Singularity spectrum of multifractal functions involving oscillating singularities. J. Fourier Anal. Appl. 4 (1998) 159–174. [Google Scholar]
  5. A. Arneodo, E. Bacry, S. Jaffard and J.F. Muzy, Oscillating singularities on Cantor sets: a grand-canonical multifractal formalism. J. Statist. Phys. 87 (1997) 179–209. [Google Scholar]
  6. A. Arneodo, E. Bacry and J.F. Muzy, The thermodynamics of fractals revisited with wavelets. Physica A 213 (1995) 232–275. [Google Scholar]
  7. E. Bacry, J.F. Muzy and A. Arneodo, Singularity spectrum of fractal signals: exact results. J. Statist. Phys. 70 (1993) 635–674. [Google Scholar]
  8. J. Bigot, Automatic landmark registration of 1D curves, in Recent advances and trends in nonparametric statistics, M. Akritas and D.N. Politis Eds., Elsevier (2003) 479–496. [Google Scholar]
  9. J. Bigot, Landmark-based registration of 1D curves and functional analysis of variance with wavelets. Technical Report TR0333, IAP (Interuniversity Attraction Pole network) (2003). [Google Scholar]
  10. L. Breiman, Bagging Predictors. Machine Learning 24 (1996) 123–140. [Google Scholar]
  11. L.D. Brown and M.G. Lo, Asymptotic equivalence of nonparametric regression and white noise. Ann. Statist. 3 (1996) 2384–2398. [Google Scholar]
  12. P. Chaudhuri and J.S.Marron, SiZer for exploration of structures in curves. J. Am. Statist. Ass. 94 (1999) 807–823. [CrossRef] [Google Scholar]
  13. P. Chaudhuri and J.S. Marron Scale space view of curve estimation. Ann. Statist. 28 (2000) 408–428. [Google Scholar]
  14. R.R. Coifman and D.L. Donoho, Translation-invariant de-noising, in Wavelets and Statistics, A. Antoniadis and G. Oppenheim, Eds., New York: Springer-Verlag. Lect. Notes Statist. 103 (1995) 125–150. [Google Scholar]
  15. I. Daubechies, Ten Lectures on Wavelets. Philadelphia, SIAM (1992). [Google Scholar]
  16. D.L. Donoho and I.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage. Biometrika 81 (1994) 425–455. [CrossRef] [MathSciNet] [Google Scholar]
  17. D.L. Donoho and I.M. Johnstone, Adapting to unknown smoothness via wavelet shrinkage. J. Am. Statist. Ass. 90 (1995) 1200–1224. [Google Scholar]
  18. D.L. Donoho and I.M. Johnstone, Minimax estimation via wavelet shrinkage. Ann. Statist. 26 (1998) 879–921. [Google Scholar]
  19. D.L. Donoho and I.M. Johnstone, Asymptotic minimality of wavelet estimators with sampled data. Stat. Sinica 9 (1999) 1–32. [Google Scholar]
  20. D.L. Donoho, I.M. Johnstone, G. Kerkyacharian and D. Picard, Wavelet shrinkage: Asymptotia? (with discussion). J. R. Statist. Soc. B 57 (1995) 301–337. [Google Scholar]
  21. N.I. Fisher and J.S. Marron, Mode testing via the excess mass estimate. Biometrika 88 (2001) 499–517. [CrossRef] [MathSciNet] [Google Scholar]
  22. T. Gasser and A. Kneip, Searching for Structure in Curve Samples. J. Am. Statist. Ass. 90 (1995) 1179–1188. [CrossRef] [Google Scholar]
  23. B. Hummel and R. Moniot, Reconstruction from zero-crossings in scale-space. IEEE Trans. Acoust., Speech, and Signal Proc. 37 (1989) 2111–2130. [Google Scholar]
  24. S. Jaffard, Mathematical Tools for Multifractal Signal Processing. Signal Processing for Multimedia, J.S Byrnes Ed., IOS Press (1999) 111–128. [Google Scholar]
  25. A. Kneip and T. Gasser, Statistical tools to analyze data representing a sample of curves. Ann. Statist. 20 (1992) 1266–1305. [CrossRef] [MathSciNet] [Google Scholar]
  26. M.R. Leadbetter, G. Lindgren and H. Rootzén, Extremes and Related Properties of Random Sequences and Processes. Springer-Verlag (1983). [Google Scholar]
  27. T. Lindeberg, Scale Space Theory in Computer Vision. Kluwer, Boston (1994). [Google Scholar]
  28. S. Mallat, Zero-Crossings of a Wavelet Transform. IEEE Trans. Inform. Theory 37 (1991) 1019–1033. [CrossRef] [MathSciNet] [Google Scholar]
  29. S. Mallat, A Wavelet Tour of Signal Processing. Academic Press (1998). [Google Scholar]
  30. S. Mallat and W.L. Hwang, Singularity Detection and Processing with Wavelets. IEEE Trans. Inform. Theory 38 (1992) 617–643. [Google Scholar]
  31. S. Mallat and S. Zhong, Characterization of Signals From Multiscale Egde. IEEE Trans. Pattern Anal. Machine Intelligence 14 (1992) 710–732. [CrossRef] [Google Scholar]
  32. S. Mallat and S. Zhong, Wavelet Transformation Maxima and Multiscale Edges, in Wavelets: A Tutorial in Theory and Applications, C.K. Chui Ed. Boston, Academic Press (1992) 66–104. [Google Scholar]
  33. S. Mallat and S. Zhong, Wavelet Maxima Representation, in Wavelets and Applications, Y. Meyer Ed. New York, Springer-Verlag (1992) 207–284. [Google Scholar]
  34. M.C. Minnotte and D.W. Scott, The mode tree: a tool for visualization of nonparametric density features. J. Computat. Graph. Statist. 2 (1993) 51–68. [CrossRef] [Google Scholar]
  35. M.C. Minnotte, D.J. Marchette and E.J. Wegman, The bumpy road to the mode forest. J. Comput. Graph. Statist. 7 (1998) 239–251. [CrossRef] [Google Scholar]
  36. M. Misiti, Y. Misiti, G. Oppenheim and J.-M. Poggi, Décomposition en ondelettes et méthodes comparatives : étude d'une courbe de charge éléctrique. Revue de Statistique Appliquée 17 (1994) 57–77. [Google Scholar]
  37. J.F. Muzy, E. Bacry and A. Arneodo, The multifractal formalism revisited with wavelets. Int. J. Bif. Chaos 4 (1994) 245–302. [Google Scholar]
  38. D. Picard and K. Tribouley, Adaptive confidence interval for pointwise curve estimation. Ann. Statist. 28 (2000) 298–335. [CrossRef] [MathSciNet] [Google Scholar]
  39. M. Raimondo, Minimax estimation of sharp change points. Ann. Statist. 26 (1998) 1379–1397. [CrossRef] [MathSciNet] [Google Scholar]
  40. J.O. Ramsay and X. Li, Curve registration. J. R. Statist. Soc. B 60 (1998) 351–363. [CrossRef] [Google Scholar]
  41. J.O. Ramsay and B.W. Silverman, Functional data analysis. New York, Springer Verlag (1997). [Google Scholar]
  42. Y. Raviv and N. Intrator, Bootstrapping with Noise: An Effective Regularization Technique. Connection Science, Special issue on Combining Estimator 8 (1996) 356–372. [Google Scholar]
  43. M. Unser, A. Aldroubi and M. Eden, On the Asymptotic Convergence of B-Spline Wavelets to Gabor Functions. IEEE Trans. Inform. Theory 38 (1992) 864–872. [CrossRef] [MathSciNet] [Google Scholar]
  44. Y. Wang, Jump and Sharp Cusp Detection by Wavelets. Biometrica 82 (1995) 385–397. [CrossRef] [Google Scholar]
  45. K. Wang and T. Gasser, Alignment of curves by dynamic time warping. Ann. Statist. 25 (1997) 1251–1276. [CrossRef] [MathSciNet] [Google Scholar]
  46. K. Wang and T. Gasser, Synchronizing sample curves nonparametrically. Ann. Statist. 27 (1999) 439–460. [CrossRef] [MathSciNet] [Google Scholar]
  47. Y.P. Wang and S.L. Lee, Scale-Space Derived From B-Splines. IEEE Trans. on Pattern Analysis and Machine Intelligence 20 (1998) 1040–1055. [CrossRef] [Google Scholar]
  48. L. Younes, Deformations, Warping and Object Comparison. Tutorial (2000) http://www.cmla.ens-cachan.fr/~younes. [Google Scholar]
  49. A.L. Yuille and T.A. Poggio, Scaling Theorems for Zero Crossings. IEEE Trans. Pattern Anal. Machine Intelligence 8 (1986) 15–25. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.