Nonlinear time series analysis
A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonline...
| Otros Autores: | , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Hoboken, NJ :
John Wiley & Sons
2019.
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| Colección: | Wiley ebooks.
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| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b40633664*spi |
Tabla de Contenidos:
- Intro; Nonlinear Time Series Analysis; Contents; Preface; 1 Why Should We Care About Nonlinearity?; 1.1 Some Basic Concepts; 1.2 Linear Time Series; 1.3 Examples of Nonlinear Time Series; 1.4 Nonlinearity Tests; 1.4.1 Nonparametric Tests; 1.4.2 Parametric Tests; Exercises; References; 2 Univariate Parametric Nonlinear Models; 2.1 A General Formulation; 2.1.1 Probability Structure; 2.2 Threshold Autoregressive Models; 2.2.1 A Two-regime TAR Model; 2.2.2 Properties of Two-regime TAR(1) Models; 2.2.3 Multiple-regime TAR Models; 2.2.4 Estimation of TAR Models; 2.2.5 TAR Modeling; 2.2.6 Examples.
- 2.2.7 Predictions of TAR Models2.3 Markov Switching Models; 2.3.1 Properties of Markov Switching Models; 2.3.2 Statistical Inference of the State Variable; 2.3.3 Estimation of Markov Switching Models; 2.3.4 Selecting the Number of States; 2.3.5 Prediction of Markov Switching Models; 2.3.6 Examples; 2.4 Smooth Transition Autoregressive Models; 2.5 Time-varying Coefficient Models; 2.5.1 Functional Coefficient AR Models; 2.5.2 Time-varying Coefficient AR Models; 2.6 Appendix: Markov Chains; Exercises; References; 3 Univariate Nonparametric Models; 3.1 Kernel Smoothing; 3.2 Local Conditional Mean.
- 3.3 Local Polynomial Fitting3.4 Splines; 3.4.1 Cubic and B-Splines; 3.4.2 Smoothing Splines; 3.5 Wavelet Smoothing; 3.5.1 Wavelets; 3.5.2 The Wavelet Transform; 3.5.3 Thresholding and Smoothing; 3.6 Nonlinear Additive Models; 3.7 Index Model and Sliced Inverse Regression; Exercises; References; 4 Neural Networks, Deep Learning, and Tree-based Methods; 4.1 Neural Networks; 4.1.1 Estimation or Training of Neural Networks; 4.1.2 An Example; 4.2 Deep Learning; 4.2.1 Deep Belief Nets; 4.2.2 Demonstration; 4.3 Tree-based Methods; 4.3.1 Decision Trees; 4.3.2 Random Forests; Exercises; References.
- 5 Analysis of Non-Gaussian Time Series5.1 Generalized Linear Time Series Models; 5.1.1 Count Data and GLARMA Models; 5.2 Autoregressive Conditional Mean Models; 5.3 Martingalized GARMA Models; 5.4 Volatility Models; 5.5 Functional Time Series; 5.5.1 Convolution FAR models; 5.5.2 Estimation of CFAR Models; 5.5.3 Fitted Values and Approximate Residuals; 5.5.4 Prediction; 5.5.5 Asymptotic Properties; 5.5.6 Application; Appendix: Discrete Distributions for Count Data; Exercises; References; 6 State Space Models; 6.1 A General Model and Statistical Inference; 6.2 Selected Examples.
- 6.2.1 Linear Time Series Models6.2.2 Time Series With Observational Noises; 6.2.3 Time-varying Coefficient Models; 6.2.4 Target Tracking; 6.2.5 Signal Processing in Communications; 6.2.6 Dynamic Factor Models; 6.2.7 Functional and Distributional Time Series; 6.2.8 Markov Regime Switching Models; 6.2.9 Stochastic Volatility Models; 6.2.10 Non-Gaussian Time Series; 6.2.11 Mixed Frequency Models; 6.2.12 Other Applications; 6.3 Linear Gaussian State Space Models; 6.3.1 Filtering and the Kalman Filter; 6.3.2 Evaluating the likelihood function; 6.3.3 Smoothing; 6.3.4 Prediction and Missing Data.
