Machine generated contents note: pt. I Bootstrap and Tests for Linearity of a Time Series -- ch. 1 Bootstrap Methods for Time Series / Soumendra Nath Lahiri -- 1.Introduction -- 2.Residual bootstrap for parametric and nonparametric models -- 3.Autoregressive-sieve bootstrap -- 4.Bootstrap for Markov chains -- 5.Block bootstrap methods -- 6.Frequency domain bootstrap methods -- 7.Mixture of two bootstrap methods -- 8.Bootstrap under long-range dependence -- Acknowledgment -- References -- ch. 2 Testing Time Series Linearity: Traditional and Bootstrap Methods / Dimitris N. Politis -- 1.Introduction -- 2.A brief survey of linearity and Gaussianity tests -- 3.Linear and nonlinear time series -- 4.AR-sieve bootstrap tests of linearity -- 5.Subsampling tests of linearity -- References -- ch. 3 The Quest for Nonlinearity in Time Series / Simone Giannerini -- 1.Introduction -- 2.Defining a linear process -- 3.Testing for nonlinearity -- 4.Conclusions -- Acknowledgments -- References --

Contents note continued: pt. II Nonlinear Time Series -- ch. 4 Modelling Nonlinear and Nonstationary Time Series / Dag Tjostheim -- 1.Introduction -- 2.Nonlinear stationary models -- 3.Linear nonstationarity -- 4.Nonlinear and nonstationary processes -- 5.Time-varying parameters and state-space models -- References -- ch. 5 Markov Switching Time Series Models / Jurgen Franke -- 1.Introduction -- 2.Markov switching autoregressions -- 3.Other Markov switching time series models -- 4.Markov switching in continuous time -- Acknowledgments -- References -- ch. 6 A Review of Robust Estimation under Conditional Heteroscedasticity / Kanchan Mukherjee -- 1.Introduction -- 2.GARCH (p,q) and GJR (1,1) models -- 3.Data analysis for the GARCH and GJR models -- 4.Value at risk and M-tests -- 5.Data analysis based on VaR -- 6.Nonlinear AR-ARCH model -- 7.Data analysis for the AR-ARCH model -- 8.Conclusions -- Acknowledgments -- References -- pt. III High Dimensional Time Series --

Contents note continued: ch. 7 Functional Time Series / Piotr Kokoszka -- 1.Introduction -- 2.The Hilbert space model for functional data -- 3.Functional autoregressive model -- 4.Weakly dependent functional time series -- 5.Further reading -- Acknowledgments -- References -- ch. 8 Covariance Matrix Estimation in Time Series / Han Xiao -- 1.Introduction -- 2.Asymptotics of sample covariances -- 3.Low-dimensional covariance matrix estimation -- 4.High-dimensional covariance matrix estimation -- Acknowledgments -- References -- pt. IV Time Series and Quantile Regression -- ch. 9 Time Series Quantile Regressions / Zhijie Xiao -- 1.An introduction to quantile regression -- 2.Quantile regression for autoregressive time series -- 3.Quantile regression for ARCH and GARCH models -- 4.Quantile regressions with dependent errors -- 5.Nonparametric and semiparametric QR models -- 6.Other dynamic quantile models -- 7.Extremal quantile regressions --

Contents note continued: 8.Quantile regression for nonstationary time series -- 9.Time series quantile regression applications -- 10.Conclusion -- Acknowledgment -- References -- pt. V Biostatistical Applications -- ch. 10 Frequency Domain Techniques in the Analysis of DNA Sequences / David S. Stoffer -- 1.Introduction -- 2.The spectral envelope -- 3.Local spectral envelope -- 4.Detection of genomic differences -- Appendix: Principal component and canonical correlation analysis for time series -- Acknowledgment -- References -- ch. 11 Spatial Time Series Modeling for fMRI Data Analysis in Neurosciences / Tohru Ozaki -- 1.Introduction -- 2.A traditional approach: Spatial and temporal covariance functions -- 3.SPM and the implied determinism -- 4.Innovation approach and the NN-ARX model -- 5.Likelihood and the significance of the assumptions -- 6.Applications to connectivity study and brain mapping -- 7.Concluding remarks -- Acknowledgement -- References --

Contents note continued: ch. 12 Count Time Series Models / Konstantinos Fokianos -- 1.Introduction -- 2.Poisson regression modeling -- 3.Poisson regression models for count time series -- 4.Other regression models for count time series -- 5.Integer autoregressive models -- 6.Conclusions -- Appendix -- Acknowledgments -- References -- pt. VI Nonstationary Time Series -- ch. 13 Locally Stationary Processes / Rainer Dahlhaus -- 1.Introduction -- 2.Time varying autoregressive processes - A deep example -- 3.Local likelihoods, derivative processes, and nonlinear models with time varying parameters -- 4.A general definition, linear processes and time varying spectral densities -- 5.Gaussian likelihood theory for locally stationary processes -- 6.Empirical spectral processes -- 7.Additional topics and further references -- Acknowledgment -- References -- ch. 14 Analysis of Multivariate Nonstationary Time Series Using the Localized Fourier Library / Hernando Ombao -- 1.Introduction --

Contents note continued: 2.Overview of SLEX analysis -- 3.Selecting the best SLEX signal representation -- 4.Classification and discrimination of time series -- 5.Summary -- Acknowledgments -- References -- ch. 15 An Alternative Perspective on Stochastic Coefficient Regression Models / Suhasini Subba Rao -- 1.Introduction -- 2.The stochastic coefficient regression model -- 3.The estimators -- 4.Testing for randomness of the coefficients in the SCR model -- 5.Asymptotic properties of the estimators -- 6.Real data analysis -- Acknowledgments -- References -- pt. VII Spatio-Temporal Time Series -- ch. 16 Hierarchical Bayesian Models for Space-Time Air Pollution Data / Sujit K. Sahu -- 1.Introduction -- 2.Hierarchical models -- 3.Prediction details -- 4.An example -- 5.Further discussion -- Acknowledgment -- Appendix: Conditional distributions for Gibbs sampling -- References -- ch. 17 Karhunen-Loeve Expansion of Temporal and Spatio-Temporal Processes / Luigi Ippoliti --

Contents note continued: 1.Introduction -- 2.Karhunen-Loeve expansion of one-dimensional processes -- 3.Multiresolution Karhunen-Loeve -- 4.Karhunen-Loeve expansion of coupled one-dimensional processes -- 5.Karhunen-Loeve expansion of spatio-temporal processes -- 6.Discussion -- Acknowledgments -- References -- ch. 18 Statistical Analysis of Spatio-Temporal Models and Their Applications / Gy. Terdik -- 1.Introduction and basic ideas -- 2.Measures for linear dependence and linearity of stationary spatial process -- 3.Models for spatial processes defined on lattices -- 4.Frequency domain approach for the estimation of CAR models -- 5.Spatio-temporal processes -- 6.Multivariate AR and STAR models -- Concluding Remarks -- Acknowledgements -- References -- pt. VIII Continuous Time Series -- ch. 19 Levy-Driven Time Series Models for Financial Data / Alexander Lindner -- 1.Introduction -- 2.Levy processes -- 3.Levy-driven CARMA(p,q) processes --

Contents note continued: 4.A continuous-time stochastic volatility model -- 5.Integrated CARMA processes and spot volatility modeling -- 6.Generalized Ornstein-Uhlenbeek processes -- 7.Continuous-time GARCH processes -- Acknowledgments -- References -- ch. 20 Discrete and Continuous Time Extremes of Stationary Processes / K.F. Turkman -- 1.Introduction -- 2.Conditions and main results -- 3.Periodogram -- Acknowledgment -- References -- pt. IX Spectral and Wavelet Methods -- ch. 21 The Estimation of Frequency / Barry G. Quinn -- 1.Introduction -- 2.Basic model -- 3.Properties of the periodogram maximizer -- 4.Links with ARMA processes -- 5.Autoregressive approximation -- 6.Pisarenko's technique -- 7.MUSIC -- 8.An efficient technique based on ARMA filtering -- 9.Maximizing the periodogram: practicalities -- 10.Discrete Fourier transform-based methods -- 11.Estimation using only the moduli of the DFT -- 12.More than one sinusoid -- 13.Complex sinusoids --

Contents note continued: 14.Related problems and areas -- References -- ch. 22 A Wavelet Variance Primer / Debashis Mondal -- 1.Introduction -- 2.Maximal overlap discrete wavelet transform -- 3.Analysis of variance via the MODWT -- 4.Definition and basic properties of wavelet variance -- 5.Basic estimators of the wavelet variance -- 6.Specialized estimators of the wavelet variance -- 7.Combining wavelet variance estimators across scales -- 8.Examples -- 9.Concluding remarks -- Acknowledgments -- References -- pt. X Computational Methods -- ch. 23 Time Series Analysis with R / Esam Mahdi -- 1.Time series plots -- 2.Base packages: stats and datasets -- 3.More linear time series analysis -- 4.Time series regression -- 5.Nonlinear time series models -- 6.Unit-root tests -- 7.Cointegration and VAR models -- 8.GARCH time series -- 9.Wavelet methods in time series analysis -- 10.Stochastic differential equations (SDEs) -- 11.Conclusion -- Acknowledgments -- A. Appendix -- References.