Exciting new developments in wavelet theory have attracted much attention and sparked new research in many fields of applied mathematics. New tools are available for efficient data compression, image analysis, and signal processing, and there is a great deal of activity in developing new wavelet methods. The same features that make wavelets useful in these fields also make wavelets a natural and attractive choice in many areas of statistical data analysis.

Essential Wavelets for Statistical Applications and Data Analysis presents an accesible, introductory survey for new wavelet analysis tools and how they can be applied to fundamental data analysis problems. A variety of problems in statistics are discussed in a non-theoretical style, with an emphasis on understanding of wavelet methods. The only technical prerequisite is basic knowledge of undergraduate calculus, linear algebra, and basic statistical theory.

Features: * Accesible, clearly presented background material provided in chapters two, three, and the appendix * Plenty of examples thoughout the book to illustrate step-by-step how the methods work * A variety of statistical application topics, such as non-parametric regression, density estimation, time series spectrial estimation, and change-point problems * A clear, intuitive style of presentation, with mathematics kept to a minimum. Emphasis on the application and understanding of wavelet methods.

The book is ideal for a broad audience which includes advanced students, graduates, and professionals in statistics. All scientists and engineers who use data analysis methods will also find the book accesible and understandable for learning about new wavelet methods and their applications in statistics.

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