Synopses & Reviews
The mathematical theory of wavelets was developed by Yes Meyer and many collaborators about ten years ago. It was designed for approximation of possibly irregular functions and surfaces and was successfully applied in data compression, turbulence analysis, and image and signal processing. Five years ago wavelet theory progressively appeared to be a powerful framework for nonparametric statistical problems. Efficient computation implementations are beginning to surface in the nineties. This book brings toghether these three streams of wavelet theory and introduces the novice in this field to these aspects. Readers interested in the theory and construction of wavelets will find in a condensed form results that are scattered in the research literature. A practitioner will be able to use wavelets via the available software code.
Synopsis
While there are many books available on wavelets, this is the first book that presents the theory and applications for a statistical audience. The first part summarizes material scattered in several sources, while the second part presents very recent research results.