时间序列分析的小波方法（英文版）|经典原版书库|Donald B.Percival,Andrew T.Walden|机械工业出版社

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时间序列分析的小波方法（英文版）

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原书名：Wavelet Methods for Time Series Analysis	原出版社：Cambridge University Press
作者： Donald B.Percival,Andrew T.Walden	出版社：机械工业出版社
译者：	丛书名：经典原版书库
出版日期：2004-5-1	上架日期：2005-10-8
ISBN：7111141180	页数：版次：1-2
开本：16开	装帧：
读者对象：本科生、研究生

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内容简介
时间序列分析是用随机过程理论和数理统计学的方法，研究随机数据序列所遵从的统计规律，用于解决科研、工程技术、金融及经济等诸多领域内的实际问题。
本书是一本由浅入深的小波分析导论，介绍了基于小波的时间序列统计分析。实践中的离散时间技术是本书的论述重点，同时对于理解和实现离散小波变换将涉及到的诸多原理与算法也进行了详细的描述。
本书图例丰富，正文附有大量练习，并在附录中给出了练习的答案。每章另备有适于课堂布置的练习。本书网站有所用时间序列与小波的材料，并可以得到用S-Plus和其他语言开发软件的信息。

目录
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Preface
Conventions and Notation
1. Introduction to Wavelets
1.0 Introduction
1.1  The Essence of a Wavelet
Comments and Extensions to Section 1.1
1.2 The Essence of Wavelet Analysis
Comments and Extensions to Section 1.2
1.3  Beyond the CWT: the Discrete Wavelet Transform
Comments and Extensions to Section 1.3
2. Review of Fourier Theory and Filters
2.0 Introduction
2.1  Complex Variables and Complex Exponentials
2.2 Fourier Transform of Infinite Sequences
2.3 Convolution]Filtering of Infinite Sequences
2.4 Fourier Transform of Finite Sequences
2.5  Circular Convolution/Filtering of Finite Sequences
2.6 Periodized Filters
Comments and Extensions to Section 2.6
2.7  Summary of Fourier Theory
2.8 Exercises
3. Orthonormal Transforms of Time Series
3.0 Introduction
3.1  Basic Theory for Orthonormal Transforms
3.2 The Projection Theorem
3.3 Complex-Valued Transforms
3.4 The Orthonormal Discrete Fourier Transform
Comments and Extensions to Section 3.4
3.5 Summary
3.6 Exercises
4. The Discrete Wavelet Transform
4.0 Introduction
4.1  Qualitative Description of the DWT
Key Facts and Definitions in Section 4.1
Comments and Extensions to Section 4.1
4.2  The Wavelet Filter
Key Facts and Definitions in Section 4.2
Comments and Extensions to Section 4.2
4.3 The Scaling Filter
Key Facts and Definitions in Section 4.3
Comments and Extensions to Section 4.3
4.4 First Stage of the Pyramid Algorithm
Key Facts and Definitions in Section 4.4
Comments and Extensions to Section 4.4
4.5 Second Stage of the Pyramid Algorithm
Key Facts and Definitions in Section 4.5
4.6 General Stage of the Pyramid Algorithm
Key Facts and Definitions in Section 4.6
Comments and Extensions to Section 4.6
4.7  The Partial Discrete Wavelet Transform
4.8  Danbechies Wavelet and Scaling Filters: Form and Phase
Key Facts and Definitions in Section 4.8
Comments and Extensions to Section 4.8
4.9  Coifiet Wavelet and Scaling Filters: Form and Phase
4.10 Example: Electrocardiogram Data
Comments and Extensions to Section 4.10
4.11 Practical Considerations
Comments and Extensions to Section 4.11
4.12 Summary
4.13 Exercises
5. The Maximal Overlap DisCrete Wavelet Transform
5.0 Introduction
5.1 Effect of Circular Shifts on the DWT
5.2 MODWT Wavelet and Scaling Filters
5.3 Basic Concepts for MODWT
Key Facts and Definitions in Section 5.3
5.4 Definition of jth Level MODWT Coefficients
Key Facts and Definitions in Section 5.4
Comments and Extensions to Section 5.4
5.5 Pyramid Algorithm for the MODWT
Key Facts and Definitions in Section 5.5
Comments and Extensions to Section 5.5
5.6 MODWT Analysis of 'Bump' Time Series
5.7 Example: Electrocardiogram Data
5.8 Example: Subtidal Sea Level Fluctuations
5.9 Example: Nile River Minima
5.10 Example: Ocean Shear Measurements
5.11 Practical Considerations
5.12 Summary
5.13 Exercises
6. The Discrete Wavelet Packet Transform
6.0 Introduction
6.1 Basic Concepts
Comments and Extensions to Section 6.1
6.2 Example: DWPT of Solar Physics Data
6.3 The Best Basis Algorithm
Comments and Extensions to Section 6.3
6.4 Example: Best Basis for Solar Physics Data
6.5 Time Shifts for Wavelet Packet Filters
Comments and Extensions to Section 6.5
6.6 Maximal Overlap Discrete Wavelet PacketTransform
6.7 Example: MODWPT of Solar Physics Data
6.8 Matching Pursuit
6.9 Example: Subtidal Sea Levels
Comments and Extensions to Section 6.9
6.10 Summary
6.11 Exercises
7. Random Variables and Stochastic Processes
7.0 Introduction
7.1 Univariate Random Variables and PDFs
7.2 Random Vectors and PDFs
7.3 A Bayesian Perspective
7.4 Stationary Stochastic Processes
7.5 Spectral Density Estimation
Comments and Extensions to Section 7.5
7.6 Definition and Models for Long Memory Processes
Comments and Extensions to Section 7.6
7.7 Nonstationary 1/f-Type Processes
Comments and Extensions to Section 7.7
7.8 Simulation of Stationary Processes
Comments and Extensions to Section 7.8
7.9 Simulation of Stationary Autoregressive Processes
7.10 Exercises
8. The Wavelet Variance
8.0 Introduction
8.1 Definition and Rationale for the Wavelet Variance
Comments and Extensions to Section 8.1
8.2 Basic Properties of the Wavelet Variance
Comments and Extensions to Section 8.2
8.3 Estimation of the Wavelet Variance
Comments and Extensions to Section 8.3
8.4 Confidence Intervals for the Wavelet Variance
Comments and Extensions to Section 8.4
8.5 Spectral Estimation via the Wavelet Variance
Comments and Extensions to Section 8.5
8.6 Example: Atomic Clock Deviates
8.7 Example: Subtidal Sea Level Fluctuations
8.8 Example: Nile River Minima
8.9 Example: Ocean Shear Measurements
8.10 Summary
8.11 Exercises
9. Analysis and Synthesis of Long Memory Processes
9.0 Introduction
9.1 Discrete Wavelet Transform of a Long Memory Process
Comments and Extensions to Section 9.1
9.2 Simulation of a Long Memory Process
Comments and Extensions to Section 9.2
9.3 MLEs for Stationary FD Processes
Comments and Extensions to Section 9.3
9.4 MLEs for Stationary or Nonstationary FD Processes
Comments and Extensions to Section 9.4
9.5 Least Squares Estimation for FD Processes
Comments and Extensions to Section 9.5
9.6 Testing for Homogeneity of Variance
Comments and Extensions to Section 9.6
9.7 Example: Atomic Clock Deviates
9.8 Example: Nile River Minima
9.9 Summary
9.10 Exercises
10. Wavelet-Based Signal Estimation
10.0 Introduction
10.1 Signal Representation via Wavelets
10.2 Signal Estimation via Thresholding
10.3 Stochastic Signal Estimation via Scaling
10.4 Stochastic Signal Estimation via Shrinkage
Comments and Extensions to Section 10.4
10.5 IID Ganssian Wavelet Coefficients
Comments and Extensions to Section 10.5
10.6 Uncorrelated Non-Gaussian Wavelet Coefficients
Comments and Extensions to Section 10.6
10.7 Correlated Gaussian Wavelet Coefficients
Comments and Extensions to Section 10.7
10.8 Clustering and Persistence of Wavelet Coefficients
10.9 Summary
10.10 Exercises
11. Wavelet Analysis of Finite Energy Signals
11.0 Introduction
11.1 Translation and Dilation
11.2 Scaling Functions and Approximation Spaces
Comments and Extensions to Section 11.2
11.3 Approximation of Finite Energy Signals
Comments and Extensions to Section 11.3
11.4 Two-Scale Relationships for Scaling Functions
11.5 Scaling Functions and Scaling Filters
Comments and Extensions to Section 11.5
11.6 Wavelet Functions and Detail Spaces
11.7 Wavelet Functions and Wavelet Filters
11.8 Multiresolution Analysis of Finite Energy Signals
11.9 Vanishing Moments
Comments and Extensions to Section 11.9
11.10 Spectral Factorization and Filter Coefficients
Comments and Extensions to Section 11.10
11.11 Summary
11.12 Exercises
Appendix. Answers to Embedded Exercises
References
Author Index
Subject Index

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本书是一本由浅入深的小波分析导论，介绍了基于小波的时间序列统计分析。实践中的离散时间技术是本书的论述重点，同时对于理解和实现离散小波变换将涉及到的诸多原理与算法也进行了详细的描述。本书图例丰富，正文附有大量练习，并在附录中给出了练习的答案。每章另备有适于课堂布置的练习。本书网站有所用时间序列与小波的材料，并可以得到用S-Plus和其他语言开发软件的信息。

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