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Spatio-Temporal Methods in Environmental Epidemiology

Chapter 10 - Why Time also Matters

Summary

The chapter contains the theory required for handling time series data. From this chapter, the reader will have gained an
understanding of the following topics:

  • That a temporal process consists of both low and high frequency components, the former playing a key role in
    determining long-term trends while the latter may be associated with shorter-term changes.
  • Techniques for the exploratory analysis of the data generated by the temporal process, including the ACF (correlogram)
    and PACF (periodogram).
  • Models for irregular (high frequency) components after the regular components (trend) have been removed.
  • Methods for forecasting, including exponential smoothing and ARIMA modelling.
  • The state space modelling approach, which sits naturally within a Bayesian setting and which provides a general
    framework for most of the classical time series models and many more besides.
  • Implementing time series processes within a Bayesian hierarchical framework.

  • R CODE

    Example 10.1

    Example 10.2

    Example 10.13

    Example 10.14

    Example 10.16

    Example 10.17

    Kings Death Data Analysis


    DATA

    Los Angeles hourly ozone data

    Los Angeles hourly ozone data-specific site


    Other

    Supplementary Material

    • ©Gavin Shaddick and James V. Zidek 2015