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A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations

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  • Stefano F. Tonellato

Abstract

We use a Bayesian multivariate time series model for the analysis of the dynamics of carbon monoxide atmospheric concentrations. The data are observed at four sites. It is assumed that the logarithm of the observed process can be represented as the sum of unobservable components: a trend, a daily periodicity, a stationary autoregressive signal and an erratic term. Bayesian analysis is performed via Gibbs sampling. In particular, we consider the problem of joint temporal prediction when data are observed at a few sites and it is not possible to fit a complex space–time model. A retrospective analysis of the trend component is also given, which is important in that it explains the evolution of the variability in the observed process.

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  • Stefano F. Tonellato, 2001. "A multivariate time series model for the analysis and prediction of carbon monoxide atmospheric concentrations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 187-200.
    • Handle: RePEc:bla:jorssc:v:50:y:2001:i:2:p:187-200
    DOI: 10.1111/1467-9876.00228
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    Cited by:

    1. Giovanna Jona Lasinio & Francesco Lagona, 2002. "Selection of the neighborhood structure for space-time Markov random field models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(3), pages 293-311, October.
    2. Yi Liu & Gavin Shaddick & James V. Zidek, 2017. "Incorporating High-Dimensional Exposure Modelling into Studies of Air Pollution and Health," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 559-581, December.
    3. Nadja A. Leith & Richard E. Chandler, 2010. "A framework for interpreting climate model outputs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(2), pages 279-296, March.

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