- 时间序列分析及其应用(第4版)(英文版)/随机数学经典
- - 作者：(美)罗伯特·沙姆韦//戴维·斯托弗|责编:陈亮
  - 出版社：世图出版公司
  - ISBN：9787519277048
  - 出版日期：2022/10/01
  - 页数：562
- 售价：59.6

内容大纲
本书是Springer统计学教程系列之一，全面地讲述了时频域方法理论。在第1版的基础上增加了不少新的内容，大量的实例结合统计软件的应用，使本书的实用性更强。本书包括分类时间序列分析、谱包络、多元谱方法、长记忆序列、非线性模型、纵向数据分析、重抽样技巧、Garch模型、随机波动性模型、小波和Monte Carlo Markov链积分方法最近发展比较迅速的话题。在本版中将这些材料划分为更小的章节，讲述更加详细，金融时间序列讲述的范围也更加广阔，包括GARCH和随机波动模型。
作者介绍
目录
Preface to the Fourth Edition
Preface to the Third Edition
1  Characteristics of Time Series
  1.1  The Nature of Time Series Data
  1.2  Time Series Statistical Models
  1.3  Measures of Dependence
  1.4  Stationary Time Series
  1.5  Estimation of Correlation
  1.6  Vector-Valued and Multidimensional Series
  Problems
2  Time Series Regression and Exploratory Data Analysis
  2.1  Classical Regression in the Time Series Context
  2.2  Exploratory Data Analysis
  2.3  Smoothing in the Time Series Context
  Problems
3  ARIMA Models
  3.1  Autoregressive Moving Average Models
  3.2  Difference Equations
  3.3  Autocorrelation and Partial Autocorrelation
  3.4  Forecasting
  3.5  Estimation
  3.6  Integrated Models for Nonstationary Data
  3.7  Building ARIMA Models
  3.8  Regression with Autocorrelated Errors
  3.9  Multiplicative Seasonal ARIMA Models
  Problems
4  Spectral Analysis and Filtering
  4.1  Cyclical Behavior and Periodicity
  4.2  The Spectral Density
  4.3  Periodogram and Discrete Fourier Transform
  4.4  Nonparametric Spectral Estimation
  4.5  Parametric Spectral Estimation
  4.6  Multiple Series and Cross-Spectra
  4.7  Linear Filters
  4.8  Lagged Regression Models
  4.9  Signal Extraction and Optimum Filtering
  4.10  Spectral Analysis of Multidimensional Series
  Problems
5  Additional Time Domain Topics
  5.1  Long Memory ARMA and Fractional Differencing
  5.2  Unit Root Testing
  5.3  GARCH Models
  5.4  Threshold Models
  5.5  Lagged Regression and Transfer Function Modeling
  5.6  Multivariate ARMAX Models
  Problems
6  State Space Models
  6.1  Linear Gaussian Model
  6.2  Filtering, Smoothing, and Forecasting
  6.3  Maximum Likelihood Estimation

  6.4  Missing Data Modifications
  6.5  Structural Models: Signal Extraction and Forecasting
  6.6  State-Space Models with Correlated Errors
    6.6.1  ARMAX Models
    6.6.2  Multivariate Regression with Autocorrelated Errors
  6.7  Bootstrapping State Space Models
  6.8  Smoothing Splines and the Kalman Smoother
  6.9  Hidden Markov Models and Switching Autoregression
  6.10  Dynamic Linear Models with Switching
  6.11  Stochastic Volatility
  6.12  Bayesian Analysis of State Space Models
  Problems
7  Statistical Methods in the Frequency Domain
  7.1  Introduction
  7.2  Spectral Matrices and Likelihood Functions
  7.3  Regression for Jointly Stationary Series
  7.4  Regression with Deterministic Inputs
  7.5  Random Coefficient Regression
  7.6  Analysis of Designed Experiments
  7.7  Discriminant and Cluster Analysis
  7.8  Principal Components and Factor Analysis
  7.9  The Spectral Envelope
  Problems
Appendix A  Large Sample Theory
  A.1  Convergence Modes
  A.2  Central Limit Theorems
  A.3  The Mean and Autocorrelation Functions
Appendix B  Time Domain Theory
  B.1  Hilbert Spaces and the Projection Theorem
  B.2  Causal Conditions for ARMA Models
  B.3  Large Sample Distribution of the AR Conditional Least Squares Estimators
  B.4  The Wold Decomposition
Appendix C  Spectral Domain Theory
  C.1  Spectral Representation Theorems
  C.2  Large Sample Distribution of the Smoothed Periodogram
  C.3  The Complex Multivariate Normal Distribution
  C.4  Integration
    C.4.1  Riemann-Stieltjes Integration
    C.4.2  Stochastic Integration
  C.5  Spectral Analysis as Principal Component Analysis
  C.6  Parametric Spectral Estimation
Appendix R  R Supplement
  R.1  First Things First
  R.2  astsa
  R.3  Getting Started
  R.4  Time Series Primer
    R.4.1  Graphics
References
Index

内容大纲

作者介绍

目录

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