Table of Contents

1. Introduction to wavelets; 2. Review of Fourier theory and filters; 3. Orthonormal transforms of time series; 4. The discrete wavelet transform; 5. The maximal overlap discrete wavelet transform; 6. The discrete wavelet packet transform; 7. Random variables and stochastic processes; 8. The wavelet variance; 9. Analysis and synthesis of long memory processes; 10. Wavelet-based signal estimation; 11. Wavelet analysis of finite energy signals; Appendix. Answers to embedded exercises; References; Author index; Subject index.

Promotional Information

This book contains detailed descriptions of the theory and algorithms needed to understand and implement discrete wavelet transforms.

Reviews

'In my opinion the book by Percival and Walden should be available in every university library, and every time-series analyst must read this book for an alternative (to Fourier) set of techniques.' T. Subba Rao, Publication of the International Statistical Institute '... would be an ideal text for a statistics doctoral student who is new to the field of wavelets ... the content, lay-out and consistency of the text mean that it should also be a valuable reference resource for the wavelet researcher.' Tim Downie, The Statistician 'The authors ... provide considerable background material, tell their story from scratch, proceed at a careful pace ... and work out detailed applications ... Recommended.' Choice