Time Series Analysis: Methods and Applications

Time Series Analysis: Methods and Applications
Erscheinungsjahr 2012
ISBN: 978-0-444-53858-1
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 152 mm x 229 mm, Gewicht: 1370 g

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.

Zielgruppe

Statisticians and scientists in various disciplines who use statistical methodology in their work

Autoren/Hrsg.

Weitere Mitwirkende

Rao, Tata Subba

Rao, Suhasini Subba

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung

Inhaltsverzeichnis

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

Über Autor(innen)

C. R. Rao, born in India is one of this century's foremost statisticians, received his education in statistics at the Indian Statistical Institute (ISI), Calcutta. Rao is currently at Penn State as Eberly Professor of Statistics and Director of the Center for Multivariate Analysis. His research has influenced not only statistics, but also the physical, social and natural sciences and engineering.