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Wavelets and Regression Analysis

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Wavelets and Statistics

Part of the book series: Lecture Notes in Statistics ((LNS,volume 103))

Abstract

Applications of the rapidly developing wavelet theory are usually limited to small dimensional cases, due to practical restrictions on the implementation of large dimensional wavelet bases. In this paper, an approach is proposed to combine wavelets and techniques of regression analysis. Regression analysis is a widely applied method for examining data and assessing relationships among variables. The resulting wavelet regression estimator is well suited for regression estimation of moderately large dimension, in particular for regressions with localized irregularities and sparse data.

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Author information

Authors and Affiliations

  1. Campus de Beaulieu, IRISA/INRIA, 35042, Rennes Cedex, France

    Qinghua Zhang

Authors
  1. Qinghua Zhang
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Editor information

Editors and Affiliations

  1. Laboratoire IMAG-LMC, University Joseph Fourier, F-38402, Grenoble Cedex, France

    Anestis Antoniadis

  2. Laboratoire MSS, University Paris-Sud, F-92405, Orsay Cedex, France

    Georges Oppenheim

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© 1995 Springer-Verlag New York

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Zhang, Q. (1995). Wavelets and Regression Analysis. In: Antoniadis, A., Oppenheim, G. (eds) Wavelets and Statistics. Lecture Notes in Statistics, vol 103. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2544-7_24

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  • DOI: https://doi.org/10.1007/978-1-4612-2544-7_24

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94564-4

  • Online ISBN: 978-1-4612-2544-7

  • eBook Packages: Springer Book Archive

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