Key Features
- 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
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.
Readership
Statisticians and scientists in various disciplines who use statistical methodology in their work
Handbook of Statistics, 1st Edition
Part 1 Bootstrap and tests for linearity of a time series
1. Bootstrap methods for time series, J-P. Kreiss and S. Lahiri.
2. Testing time series linearity: traditional and bootstrap methods, A. Berg, T. McMurry
and D. N. Politis
3. The quest for nonlinearity in Time Series, S. Giannerini
Part II Nonlinear time series
4. Modelling nonlinear and nonstationary time series, D. Tjøstheim
5. Markov switching time series models, J. Franke
6. A review of robust estimation under conditional heteroscedasticity, K. Mukherjee
Part III High dimensional time series
7. Functional time series, S. Hörmann and P. Kokoszka
8. Covariance matrix estimation in Time Series, W. B. Wu and H. Xiao
Part IV Time series and quantile regression
9. Time series quantile regressions, Z. Xiao
Part V Biostatistical applications
10. Frequency domain techniques in the analysis of DNA sequences, D. Stoffer
11. Spatial time series modelling for fMRI data analysis in neurosciences, T. Ozaki
12. Count time series models, K. Fokianos
Part VI Nonstationary time series
13. Locally stationary processes, R. Dahlhaus
14. Analysis of multivariate non-stationary time series using the localised Fourier Library,
H. Ombao
15. An alternative perspective on stochastic coefficient regression models, S. Subba Rao
Part VII Spatio-Temporal Time Series
16. Hierarachical Bayesian models for space-time air pollution data, S. Sahu
17. Karhunen-Loeve expansion for temporal and spatio-temporal processes, L. Fontanella
and L. Ippoliti
18. Statistical analysis of spatio-temporal models and their applications, T. Subba Rao and
G. Terdik
Part VIII Continuous time series
19. Lévy-driven time series models for financial data, P. Brockwell and A. Lindner
20. Discrete and continuous time extremes of stationary processes, K. F. Turkman
Part IX Spectral and Wavelet Methods
21. The estimation of Frequency, B. G. Quinn
22. A wavelet variance primer, D. B. Percival and D. Mondal
Part X Computational methods
23. Time Series Analysis with R, A. I. McLeod, H. Yu and E. Mahdi
