Handbook of Statistics: Time Series Analysis: Methods and Applications

Handbook of Statistics: Time Series Analysis: Methods and Applications

by Elsevier Science
     
 

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ISBN-10: 0444538585

ISBN-13: 9780444538581

Pub. Date: 07/27/2012

Publisher: Elsevier Science

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

Overview

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.

    • 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

    Product Details

    ISBN-13:
    9780444538581
    Publisher:
    Elsevier Science
    Publication date:
    07/27/2012
    Series:
    Handbook of Statistics Series, #30
    Pages:
    774
    Product dimensions:
    6.20(w) x 9.10(h) x 1.40(d)

    Table of Contents

    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

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