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Multivariate Intensity Estimation via Hyperbolic Wavelet Selection

Abstract : We propose a new statistical procedure able in some way to overcome the curse of dimensionality without structural assumptions on the function to estimate. It relies on a least-squares type penalized criterion and a new collection of models built from hyperbolic biorthogonal wavelet bases. We study its properties in a unifying intensity estimation framework, where an oracle-type inequality and adaptation to mixed smoothness are shown to hold. Besides, we describe an algorithm for implementing the estimator with a quite reasonable complexity.
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Contributor : Nathalie Akakpo <>
Submitted on : Wednesday, November 23, 2016 - 8:21:56 PM
Last modification on : Saturday, March 28, 2020 - 2:22:29 AM
Document(s) archivé(s) le : Tuesday, March 21, 2017 - 6:16:38 AM


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  • HAL Id : hal-01392920, version 2
  • ARXIV : 1611.07237


Nathalie Akakpo. Multivariate Intensity Estimation via Hyperbolic Wavelet Selection. 2016. ⟨hal-01392920v2⟩



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