Handbook of Statistics book cover

Handbook of Statistics

Time Series Analysis: Methods and Applications

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.

Audience

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

Included in series
Handbook of Statistics

Hardbound, 776 Pages

Published: May 2012

Imprint: North-holland

ISBN: 978-0-444-53858-1

Reviews

  • "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


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

Advertisement

advert image