Description
The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments.The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience.- Comprehensively presents the various aspects of statistical methodology- Discusses a wide variety of diverse applications and recent developments- Contributors are internationally renowened experts in their respective areas
Table of Contents
1. Bootstrap methods for time series2. Testing time series linearity: traditional and bootstrap methods3. The quest for nonlinearity in Time Series4. Modelling nonlinear and nonstationary time series,5. Markov switching time series models6. A review of robust estimation under conditional heteroscedasticity7. Functional time series8. Covariance matrix estimation in Time Series9. Time series quantile regressions10. Frequency domain techniques in the analysis of DNA sequences11. Spatial time series modelling for fMRI data analysis in neurosciences12. Count time series models13. Locally stationary processes14. Analysis of multivariate non-stationary time series using the localised Fourier Library15. An alternative perspective on stochastic coefficient regression models16. Hierarachical Bayesian models for space-time air pollution data17. Karhunen-Loeve expansion for temporal and spatio-temporal processes18. Statistical analysis of spatio-temporal models and their applications19. Lévy-driven time series models for financial data20. Discrete and continuous time extremes of stationary processesn21. The estimation of Frequency22. A wavelet variance primer23. Time Series Analysis with R
