A comprehensive, step-by-step introduction to wavelets in statistics.

What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for "time-scale" applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly "discovered" mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory, Vidakovic compiles, organizes, and explains in depth research data previously available only in disparate journal articles. He carefully balances both statistical and mathematical techniques, supplementing the material with a wealth of examples, more than 100 illustrations, and extensive references—with data sets and S-Plus wavelet overviews made available for downloading over the Internet. Both introductory and data-oriented modeling topics are featured, including:

• Continuous and discrete wavelet transformations.
• Statistical optimality properties of wavelet shrinkage.
• Theoretical aspects of wavelet density estimation.
• Bayesian modeling in the wavelet domain.
• Properties of wavelet-based random functions and densities.
• Several novel and important wavelet applications in statistics.
• Wavelet methods in time series.

  Accessible to anyone with a background in advanced calculus and algebra, Statistical Modeling by Wavelets promises to become the standard reference for statisticians and engineers seeking a comprehensive introduction to an emerging field.