• Bernoulli
  • Volume 10, Number 6 (2004), 1053-1105.

Regression in random design and warped wavelets

Gérard Kerkyacharian and Dominique Picard

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We consider the problem of estimating an unknown function f in a regression setting with random design. Instead of expanding the function on a regular wavelet basis, we expand it on the basis { ψ jk (G),j,k} warped with the design. This allows us to employ a very stable and computable thresholding algorithm. We investigate the properties of this new basis. In particular, we prove that if the design has a property of Muckenhoupt type, this new basis behaves quite similarly to a regular wavelet basis. This enables us to prove that the associated thresholding procedure achieves rates of convergence which have been proved to be minimax in the uniform design case.

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Bernoulli, Volume 10, Number 6 (2004), 1053-1105.

First available in Project Euclid: 21 January 2005

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maxisets Muckenhoupt weights nonparametric regression random design warped wavelets wavelet thresholding


Kerkyacharian, Gérard; Picard, Dominique. Regression in random design and warped wavelets. Bernoulli 10 (2004), no. 6, 1053--1105. doi:10.3150/bj/1106314850.

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