Automatic Autocorrelation and Spectral Analysis

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Springer ebook

Automatic Autocorrelation and Spectral Analysis

Broersen, Piet M.T.

2006, XII, 298 p., 104 illus., Hardcover

ISBN: 978-1-84628-328-4

Table of contents

Introduction.- Basic Concepts.- Periodogram and Lagged Product Autocorrelation.- ARMA Theory.- Relations for Time Series Models.- Estimation of Time Series Models.- AR Order Selection.- MA and ARMA Order Selection.- ARMASA Toolbox with Applications.- Advanced Topics in Time Series Estimation. ARMASA Toolbox.

About this textbook

Automatic Autocorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively.

In the current practice of spectral analysis, subjective decisions have to be made all of which influence the final spectral estimate and mean that different analysts obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that solution is only acceptable if it is close to the best attainable accuracy for most types of stationary data.

Automatic Autocorrelation and Spectral Analysis describes a method which fulfils the near-optimal-solution criterion. It takes advantage of greater computing power and robust algorithms to produce enough models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the best (and often the best) will be selected automatically. The data themselves suggest their best representation but should the analyst wish to intervene, alternatives can be provided. Written for graduate signal processing students and for researchers and engineers using time series analysis for practical applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers:

• tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models;
• extensive support for the MATLAB® ARMAsel toolbox;
• applications showing the methods in action;
• appropriate mathematics for students to apply the methods with references for those who wish to develop them further.

Written for:

Graduate students in various areas of signal processing; researchers studying the application of time series analysis for random signals; engineers working with random data in practice

Keywords:

ARMAsel

Autocorrelation Estimation

MATLAB®

Order Selection

Random Data

Signal Processing

Spectral Analysis

Statistical Signal Processing

Stochastic Data

Stochastic Processes

Time Series Analysis

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Automatic Autocorrelation and Spectral Analysis
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关键词：correlation Automatic Analysis Spectral autocorr Analysis Spectral Automatic

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【书名】 Automatic Autocorrelation and Spectral Analysis
【作者】 Broersen, Piet M.T.
【出版社】Springer
【版本】
【出版日期】2006
【文件格式】PDF
【文件大小】2.78 MB
【页数】298
【ISBN出版号】ISBN: 978-1-84628-328-4
【资料类别】计量经济学，统计学，时间序列分析
【市面定价】79.50 Dollars (Amazon Hardcover)
【扫描版还是影印版】影印版
【是否缺页】完整
【关键词】ARMA，Autocorrelation Estimation，Spectral Analysis， Time Series Analysis
【内容简介】

Automatic Autocorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively.In the current practice of spectral analysis, subjective decisions have to be made all of which influence the final spectral estimate and mean that different analysts obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that solution is only acceptable if it is close to the best attainable accuracy for most types of stationary data.Automatic Autocorrelation and Spectral Analysis describes a method which fulfils the near-optimal-solution criterion. It takes advantage of greater computing power and robust algorithms to produce enough models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the best (and often the best) will be selected automatically. The data themselves suggest their best representation but should the analyst wish to intervene, alternatives can be provided. Written for graduate signal processing students and for researchers and engineers using time series analysis for practical applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers:tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models; extensive support for the MATLAB® ARMAsel toolbox; applications showing the methods in action; appropriate mathematics for students to apply the methods with references for those who wish to develop them further.Written for: Graduate students in various areas of signal processing; researchers studying the application of time series analysis for random signals; engineers working with random data in practice

 
【目录】

Table of contents

1 Introduction.-

2 Basic Concepts.-

3 Periodogram and Lagged Product Autocorrelation.-

4 ARMA Theory.-

5 Relations for Time Series Models.-

6 Estimation of Time Series Models.-

7 AR Order Selection.-

8 MA and ARMA Order Selection.-

9 ARMASA Toolbox with Applications.-

10 Advanced Topics in Time Series Estimation.

【书评】包括时间序列分析中大部分内容，比较好的一本书

好贵啊，赶快挣钱