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A new method to build spatio-temporal covariance functions: analysis of ozone data

Author

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  • Mehdi Omidi

    (Tarbiat Modares University)

  • Mohsen Mohammadzadeh

    (Tarbiat Modares University)

Abstract

Statistical analysis of natural phenomena with spatial and temporal correlations requires the specification of the correlation structure via a covariance function. A separable spatio-temporal covariance function is usually used for the ease of application. Nonetheless, the separability of the spatio-temporal covariance function can be unrealistic in many settings, where it is required to use a non-separable spatio-temporal covariance function. In this paper, the role of Stieltjes transformation in the construction of non-separable spatio-temporal covariance function is investigated. Then, structural copula function is applied to construct a family of non-separable spatio-temporal covariance function. Afterwards, it is proved that this family of covariance functions does not possess any dimple which exists in some Gneiting’s models. Finally, a modified genetic algorithm is applied to explore the spatio-temporal correlation structure of Ozone data in Tehran, Iran.

Suggested Citation

  • Mehdi Omidi & Mohsen Mohammadzadeh, 2016. "A new method to build spatio-temporal covariance functions: analysis of ozone data," Statistical Papers, Springer, vol. 57(3), pages 689-703, September.
    • Handle: RePEc:spr:stpapr:v:57:y:2016:i:3:d:10.1007_s00362-015-0674-2
    DOI: 10.1007/s00362-015-0674-2
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    References listed on IDEAS

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