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Non-Gaussian spatiotemporal modelling through scale mixing


  • Thaís C. O. Fonseca
  • Mark F. J. Steel


We construct non-Gaussian processes that vary continuously in space and time with nonseparable covariance functions. Starting from a general and flexible way of constructing valid nonseparable covariance functions through mixing over separable covariance functions, the resulting models are generalized by allowing for outliers as well as regions with larger variances. We induce this through scale mixing with separate positive-valued processes. Smooth mixing processes are applied to the underlying correlated processes in space and in time, thus leading to regions in space and time of increased spread. An uncorrelated mixing process on the nugget effect accommodates outliers. Posterior and predictive Bayesian inference with these models is implemented through a Markov chain Monte Carlo sampler. An application to temperature data in the Basque country illustrates the potential of this model in the identification of outliers and regions with inflated variance, and shows that this improves the predictive performance. Copyright 2011, Oxford University Press.

Suggested Citation

  • Thaís C. O. Fonseca & Mark F. J. Steel, 2011. "Non-Gaussian spatiotemporal modelling through scale mixing," Biometrika, Biometrika Trust, vol. 98(4), pages 761-774.
  • Handle: RePEc:oup:biomet:v:98:y:2011:i:4:p:761-774

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    References listed on IDEAS

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    Cited by:

    1. Zareifard, Hamid & Jafari Khaledi, Majid, 2013. "Non-Gaussian modeling of spatial data using scale mixing of a unified skew Gaussian process," Journal of Multivariate Analysis, Elsevier, vol. 114(C), pages 16-28.

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