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Functional Statistics and Applications pp 29–50Cite as

BlockShrink Wavelet Density Estimator in ϕ-Mixing Framework

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Part of the book series: Contributions to Statistics ((CONTRIB.STAT.))

Abstract

We study the integrated L 2-risk, of a wavelet BlockShrink density estimator based on dependent observations. We prove that the BlockShrink estimator is adaptive in a class of Sobolev space with unknown regularity for uniformly mixing processes with arithmetically decreasing mixing coefficients.

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Author information

Authors and Affiliations

  1. Ecole Nationale des Sciences Appliquées (ENSA) de Khouribga, Université Hassan 1er, Settat, Morocco

    Mohammed Badaoui

  2. LaMSD et URAC 04, Département de Mathématiques et Informatique, Faculté des Sciences, Université Mohamed 1er, Oujda, Morocco

    Mohammed Badaoui & Noureddine Rhomari

Authors
  1. Mohammed Badaoui
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  2. Noureddine Rhomari
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Corresponding author

Correspondence to Noureddine Rhomari .

Editor information

Editors and Affiliations

  1. LMPA, Université du Littoral Côte d'Opale, Calais, France

    Elias Ould Saïd

  2. National School of Applied Sciences, Cadi Ayyad University, Marrakesh, Morocco

    Idir Ouassou

  3. Faculty of Science of Man and Society, Grenoble-Alpes University, Grenoble, France

    Mustapha Rachdi

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Badaoui, M., Rhomari, N. (2015). BlockShrink Wavelet Density Estimator in ϕ-Mixing Framework. In: Ould Saïd, E., Ouassou, I., Rachdi, M. (eds) Functional Statistics and Applications. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-22476-3_2

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