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时间序列的理论与方法(第2版)(英文版) 布雷克韦尔世界图书出版公司正版新书

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作者布雷克韦尔

出版社世界图书出版公司

ISBN9787510094712

出版时间2015-05

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货号3261203

上书时间2024-07-13

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品相描述：全新

商品描述: 作者简介
Peter J.Brockwell(P.J.布雷韦尔，美国)是靠前知名学者，在数学和物理学界享有盛誉。本书凝聚了作者多年科研和教学成果，适用于科研工作者、高校教师和研究生。

目录
Preface to the Second Edition
Preface to the First Edition
CHAPTER 1 Stationary Time Series
  1.1 Examples of Time Series
  1.2 Stochastic Processes
  1.3 Stationarity and Strict Stationarity
  1.4 The Estimation and Elimination of Trend and Seasonal Components
  !.5 The Autocovariance Function of a Stationary Process
  1.6 The Multivariate Normal Distribution
  1.7 Applications of Kolmogorov's Theorem Problems
CHAPTER 2 Hilbert Spaces
  2.1 Inner-Product Spaces and Their Properties
  2.2 Hilbert Spaces
  2.3 The Projection Theorem
  2.4 Orthonormal Sets
  2.5 Projection in R
  2.6 Linear Regression and the General Linear Model
  2.7 Mean Square Convergence, Conditional Expectation and Best Linear Prediction in L2(t, P)
  2.8 Fourier Series
  2.9 Hilbert Space Isomorphisms
  2.10* The Completeness of L2 (D, ,, P)
  2.11* Complementary Results for Fourier Series Problems
CHAPTER 3 Stationary ARMA Processes
  3.1 Causal and Invertible ARMA Processes
  3.2 Moving Average Processes of Infinite Order
  3.3 Computing the Autocovariance Function of an ARMA(p, q) Process
  3.4 The Partial Autocgrrelation Function
  3.5 The Autocovariance Generating Function
  3.6* Homogeneous Linear Difference Equations with Constant Coefficients Problems
CHAPTER 4 The Spectral Representation of a Stationary Process
  4.1 Complex-Valued Stationary Time Series
  4.2 The Spectral Distribution of a Linear Combination of Sinusoids
  4.3 Herglotz's Theorem
  4.4 Spectral Densities and ARMA Processes
  4.5* Circulants and Their Eigenvalues
  4.6* Orthogonal Increment Processes on [- n, n]
  4.7* Integration with Respect to an Orthogonal Increment Process
  4.8* The Spectral Representation
  4.9* Inversion Formulae
  4.10* Time-lnvariant Linear Filters
  4.11* Properties of the Fourier Approximation hn to I(v,w) Problems
CHAPTER 5 Prediction of Stationary Processes
  5.1 The Prediction Etuations in the Time Domain
  5.2 Recursive Methols for Computing Best Linear Predictors
  5.3 Recursive Prediction of an ARMA(p, q) Process
  5.4 Prediction of a Stationary Gaussian Process; Prediction Bounds
  5.5 Prediction of a Causal lnvertible ARMA Process in Terms ofXj,-∞   5.6* Prediction in the Frequency Domain
  5.7* The Wold Decomposition
  5.8* Kolmogorov's Formula
   Problems
CHAPTER 6* Asymptotic Theory
  6.1 Convergence in Probability
  6.2 Convergence in r'h Mean, r > 0
  6.3 Convergence in Distribution
  6.4 Central Limit Theorems and Related Results Problems
CHAPTER 7 Estimation of the Mean and the Autocovariance Function
  7.1 Estimation of
  7.2 Estimation ofγ(.) and p(.)
  7.3* Derivation of the Asymptotic Distributions Problems
CHAPTER 8 Estimation for ARMA Models
  8.1 The Yule-Walker Equations and Parameter Estimation for Autoregressive Processes
  8.2 Preliminary Estimation for Autoregressive Processes Using the Durbin-Levinson Algorithm
  8.3 Preliminary Estimation for Moving Average Processes Using the Innovations Algorithm
  8.4 Preliminary Estimation for ARMA(p, q) Processes
  8.5 Remarks on Asymptotic Efficiency
  8.6 Recursive Calculation of the Likelihood of an Arbitrary Zero-Mean Gaussian Process
  8.7 Maximum Likelihood and Least Squares Estimation for ARMA Processes
  8.8 Asymptotic Properties of the Maximum Likelihood Estimators
  8.9 Confidence Intervals for the Parameters of a Causal Invertible ARMA Process
  8.10* Asymptotic Behavior of the Yule-Walker Estimates
  8.11 * Asymptotic Normality of Parameter Estimators Problems
CHAPTER 9 Model Building and Forecasting with ARIMA Processes
  9.1 ARIMA Models for Non-Stationary Time Series
  9.2 Identification Techniques
  9.3 Order Selection
  9.4 Diagnostic Checking
  9.5 Forecasting ARIMA Models
  9.6 Seasonal ARIMA Models Problems
CHAPTER 10 Inference for the Spectrum of a Stationary Process
  10.1 The Periodogram
  10.2 Testing for the Presence of Hidden Periodicities
  10.3 Asymptotic Properties of the Periodogram
  10.4 Smoothing the Periodogram
  10.5 Confidence Intervals for the Spectrum
  10.6 Autoregressive, Maximum Entropy, Moving Average and Maximum Likelihood ARMA Spectral Estimators
  10.7 The Fast Fourier Transform (FFT) Algorithm
  10.8* Derivation of the Asymptotic Behavior of the Maximum Likelihood and Least Squares Estimators of the Coefficients of an ARMA Process Problems
CHAPTER 11 Multivariate Time Series
  11.1 Second Order Properties of Multivariate Time Series
  11.2 Estimation of the Mean and Covariance Function
  11.3 Multivariate ARMA Processes
  11.4 Best Linear Predictors of Second Order Random Vectors
  11.5 Estimation for Multivariate ARMA Processes
  11.6 The Cross Spectrum
  11.7 Estimating the Cross Spectrum
  11.8* The Spectral Representation of a Multivariate Stationary Time Series Problems
CHAPTER 12 State-Space Models and the Kalman Recursions
  12.1 State-Space Models
  12.2 The Kalman Recursions
  12.3 State-Space Models with Missing Observations
  12.4 Controllability and Observability
  12.5 Recursive Bayesian State Estimation Problems
CHAPTER 13 Further Topics
  13.1 Transfer Function Modelling
  13.2 Long Memory Processes
  13.3 Linear Processes with Infinite Variance
  13.4 Threshold Models Problems
Appendix: Data Sets
Bibliography
Index

内容摘要
由布雷克韦尔著的《时间序列的理论与方法(第2版)(英文版)》是一部有关时间序列的高等教材，这版对原来的版本通篇做了大量的修订和扩充，包括增加了全新的一章讲述状态空间。内容详尽，包括了学习时间序列能够经常用到的所有方法，书的一开始从引进Hilbert空间开始，紧接着平稳ARMA过程及其他。第10章有关平稳过程的谱推断很有新意，考虑频率已知和未知的周期性各种检验。