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Titlebook: Essential Wavelets for Statistical Applications and Data Analysis; R. Todd Ogden Book 1997 Springer Science+Business Media New York 1997 E

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發(fā)表于 2025-3-21 18:42:12 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Essential Wavelets for Statistical Applications and Data Analysis
編輯R. Todd Ogden
視頻videohttp://file.papertrans.cn/316/315568/315568.mp4
概述Includes supplementary material:
圖書封面Titlebook: Essential Wavelets for Statistical Applications and Data Analysis;  R. Todd Ogden Book 1997 Springer Science+Business Media New York 1997 E
描述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.
出版日期Book 1997
關(guān)鍵詞Estimator; Invariant; Regression; Signal; Wavelet; algorithm; calculus; data analysis; discrete Fourier tran
版次1
doihttps://doi.org/10.1007/978-1-4612-0709-2
isbn_softcover978-1-4612-6876-5
isbn_ebook978-1-4612-0709-2
copyrightSpringer Science+Business Media New York 1997
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Basic Smoothing Techniques,the existing techniques in use. Though there are many methods currently in use for these applications, this chapter will focus on only two of them: kernel smoothing and orthogonal series estimation. This background should provide a useful lead-in to a discussion of wavelet methods for function estim
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Some Practical Issues,than as a powerful method for solving practical problems. In fact, much of the beauty of wavelet analysis lies in its widespread applications. This chapter will discuss important issues that arise in moving wavelets from theory to practice.
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Data Adaptive Wavelet Thresholding,where Section 7.1 left off, focusing on the nonparametric regression situation. The ideas described here could also be adapted for use in density estimation or other types of function estimation. To focus attention on the methods described in this chapter, it will be assumed throughout that an ortho
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Essential Wavelets for Statistical Applications and Data Analysis
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Essential Wavelets for Statistical Applications and Data Analysis978-1-4612-0709-2
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