Time Series Analysis: Methods and Applications

Surveys recent developments in time series analysis

Tata Subba Rao (Volume editor), Suhasini Subba Rao (Volume editor), C.R. Rao (Volume editor)

9780444538581

Hardback, published 18 May 2012

776 pages
22.9 x 15.1 x 3.9 cm, 1.37 kg

"Referring to earlier volumes in the venerable series Handbook of Statistics- -v.3 (1983) and v.5 (1985)--the three editors preface this 30th volume by describing the explosion of developments since those books were published. Initial chapters cover topics that were in their infancy 25 years ago, including bootstrap methods and tests for linearity of a time series. Following is coverage of methods of modeling nonlinear time series, functional data and high-dimensional time series, applications to biological and neurological sciences, nonstationary time series, spatio- temporal models, continuous time series, and spectral and wavelet methods for the analysis of signals, among other topics. The editors are affiliated as follows: Tata Subba Rao (U. of Manchester, UK), Suhasini Subba Rao (Texas A&M U., US) and C.R. Rao (U. of Hyderabad Campus, India)." --Reference and Research Book News, October 2012

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.

1. Bootstrap methods for time series
2. Testing time series linearity: traditional and bootstrap methods
3. The quest for nonlinearity in Time Series
4. Modelling nonlinear and nonstationary time series,
5. Markov switching time series models
6. A review of robust estimation under conditional heteroscedasticity
7. Functional time series
8. Covariance matrix estimation in Time Series
9. Time series quantile regressions
10. Frequency domain techniques in the analysis of DNA sequences
11. Spatial time series modelling for fMRI data analysis in neurosciences
12. Count time series models
13. Locally stationary processes
14. Analysis of multivariate non-stationary time series using the localised Fourier Library
15. An alternative perspective on stochastic coefficient regression models
16. Hierarachical Bayesian models for space-time air pollution data
17. Karhunen-Loeve expansion for temporal and spatio-temporal processes
18. Statistical analysis of spatio-temporal models and their applications
19. Lévy-driven time series models for financial data
20. Discrete and continuous time extremes of stationary processesn
21. The estimation of Frequency
22. A wavelet variance primer
23. Time Series Analysis with R

Subject Areas: Probability & statistics [PBT]