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Abstract

With a basic understanding of wavelet theory and a knowledge of the practical issues involved in applying wavelets to observed data, we are now ready to extend the basic methods of Chapter 3 to more sophisticated techniques on a wide variety of applications. Perhaps the most common wavelet application in statistics is nonparametric regression, which is covered in some depth in Section 7.1. This will serve as a groundwork for other applications treated later in this chapter: density estimation, estimation of the spectral density in time series, and the general change-point problem. Extensions of these methods will be given in the context of nonparametric regression in Chapter 8.

Keywords

Spectral Density Wavelet Coefficient Nonparametric Regression Spectral Density Function Hard Thresholding 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • R. Todd Ogden
    • 1
  1. 1.Department of StatisticsUniversity of South CarolinaColumbiaUSA

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