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Essential Wavelets for Statistical Applications and Data Analysis

  • R. Todd Ogden

Table of contents

  1. Front Matter
    Pages i-xviii
  2. R. Todd Ogden
    Pages 1-27
  3. R. Todd Ogden
    Pages 29-47
  4. R. Todd Ogden
    Pages 49-58
  5. R. Todd Ogden
    Pages 59-88
  6. R. Todd Ogden
    Pages 89-102
  7. R. Todd Ogden
    Pages 103-117
  8. R. Todd Ogden
    Pages 119-142
  9. R. Todd Ogden
    Pages 143-165
  10. R. Todd Ogden
    Pages 167-184
  11. Back Matter
    Pages 185-206

About this book

Introduction

I once heard the book by Meyer (1993) described as a "vulgarization" of wavelets. While this is true in one sense of the word, that of making a sub­ ject popular (Meyer's book is one of the early works written with the non­ specialist in mind), the implication seems to be that such an attempt some­ how cheapens or coarsens the subject. I have to disagree that popularity goes hand-in-hand with debasement. is certainly a beautiful theory underlying wavelet analysis, there is While there plenty of beauty left over for the applications of wavelet methods. This book is also written for the non-specialist, and therefore its main thrust is toward wavelet applications. Enough theory is given to help the reader gain a basic understanding of how wavelets work in practice, but much of the theory can be presented using only a basic level of mathematics. Only one theorem is for­ mally stated in this book, with only one proof. And these are only included to introduce some key concepts in a natural way.

Keywords

Estimator Invariant Regression Signal Wavelet algorithm calculus data analysis discrete Fourier transform function linear optimization mathematics proof theorem wavelets

Authors and affiliations

  • R. Todd Ogden
    • 1
  1. 1.Department of StatisticsUniversity of South CarolinaColumbiaUSA

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