Handbook of Statistics

Time Series Analysis: Methods and Applications

Edited by

  • Tata Subba Rao, School of Mathematics, University of Manchester, UK and C.R.Rao's Advanced Institute of Mathematics, Statistics and Computer Science ((C.R.Rao AIMSCS), Hyderabad, India
  • Suhasini Subba Rao
  • C.R. Rao, The Pennsylvania State University, PA, USA

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


Book information

  • Published: May 2012
  • Imprint: NORTH-HOLLAND
  • ISBN: 978-0-444-53858-1


"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

Table of Contents

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