Chapter 1 presented the bare necessities for understanding the basic principles of wavelet analysis, presenting the concepts through the simplest example, the Haar system. With a good understanding of these principles, it is possible to skip forward to later chapters dealing with statistical analysis, but before treating more advanced statistical applications of wavelet analysis, a more thorough and general treatment of wavelets is useful. This chapter will give more insight into some of the advantages inherent in wavelet analysis, describing basic algorithms, and time-frequency localization concepts. It finishes up with a more complete development of the wavelet examples mentioned in Section 1.3.


Wavelet Coefficient Scaling Function Window Function Mother Wavelet Continuous Wavelet 
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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