范剑青著的《非线性时间序列(非参数与参数方法影印版)(精)/国外数学名著系列》论述当代统计方法和非线性时间序列分析，着重阐述过去十年发展起来的非参数和半参数技术。主要内容包括相空间、频域及时域中的建模技术；为说明参数方法和非参数方法在时间序列数据分析中的一体性，本书给出某些参数化非线性模型的最新论述，如ARCH／GARCH模型和阂值模型；以及关于ARMA模型的一个简洁观点。本书始终使用实际应用中得到的数据，阐明如何借助非参数方法揭示高维数据的局部结构。本书还介绍了一些重要的技术工具。
本书适合研究生，时间序列分析方面的实际工作者，该领域不同程度的研究人员。本书在统计界和诸如计量经济学、实证金融学、群体生物学及生态学之类的其他广泛领域都有其价值。阅读本书需要概率论和统计的基本知识。

Preface
1 Introduction
1.1 Examples of Time Series
1.2 Objectives of Time Series Analysis
1.3 Linear Time Series Models
1.3.1 White Noise Processes
1.3.2 AR Models
1.3.3 MA Models
1.3.4 ARMA Models
1.3.5 ARIMA Models
1.4 What Is a Nonlinear Time Series?
1.5 Nonlinear Time Series Models
1.5.1 A Simple Example
1.5.2 ARCH Models
1.5.3 Threshold Models
1.5.4 Nonparametric Autoregressive Models
1.6 From Linear to Nonlinear Models
1.6.1 Local Linear Modeling
1.6.2 Global Spline Approximation
1.6.3 Goodness-of-Fit Tests
1.7 Further Reading
1.8 Software Implementations
2 Characteristics of Time Series
2.1 Stationarity
2.1.1 Definition
2.1.2 Stationary ARMA Processes
2.1.3 Stationary Gaussian Processes
2.1.4 Ergodic Ncnlinear Medels*
2.1.5 Stationary ARCH Processes
2.2 Autocorrelation
2.2.1 Autocovariance and Autocorrelation
2.2.2 Estimation of ACVF and ACF
2.2.3 Partial Autocorrelation
2.2.4 ACF Plots, PACF Plots, and Examples
2.3 Spectral Distributions
2.3.1 Periodic Processes
2.3.2 Spectral Densities
2.3.3 Linear Filters
2.4 Periodogram
2.4.1 Discrete Fourier Transforms
2.4.2 Periodogram
2.5 Long-Memory Processes*
2.5.1 Fractionally Integrated Neise
2.5.2 Fractionally Integrated ARMA processes
2.6 Mixing*
2.6.1 Mixing Conditions
2.6.2 Inequalities
2.6.3 Limit Theorems for a-Mixing Processes
2.6.4 A Central Limit Theorem for Nonparametric Regres-
sion
2.7 Complements
2.7.1 Proof of Theorem 2.5(i)
2.7.2 Proof of Preposition 2.3(i)
2.7.3 Proof of Theorem 2.9
2.7.4 Proof of Theorem 2.10
2.7.5 Proof of Theorem 2.13
2.7.6 Proof of Theorem 2.14
2.7.7 Procf of Theorem 2.22
2.8 Additional Bibliographical Nctes
3 ARMA Modeling and Forecasting
3.1 Models and Background
3.2 The Best Linear Prediction--Prewhitening
3.3 Maximum Likelihood Estimation
3.3.1 Estimators
3.3.2 Asymptotic Properties
3.3.3 Confidence Intervals
4 Parametric Nonlinear Time Series Models
5 Nonparametric Density Estimation
6 Smooting in Time Series
7 Spectral Density Estimation and Its Applications
8 Nonparametric Models
9 Model Validation
10 Nonlinear Prediction