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

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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.

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Authors and Affiliations

  1. Department of Statistics, Tarbiat Modares University, Tehran, Iran

    Mehdi Omidi & Mohsen Mohammadzadeh

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  1. Mehdi Omidi
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  2. Mohsen Mohammadzadeh
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Correspondence to Mohsen Mohammadzadeh.

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Omidi, M., Mohammadzadeh, M. A new method to build spatio-temporal covariance functions: analysis of ozone data. Stat Papers 57, 689–703 (2016). https://doi.org/10.1007/s00362-015-0674-2

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  • DOI: https://doi.org/10.1007/s00362-015-0674-2

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