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

Mehdi Omidi () and Mohsen Mohammadzadeh ()
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Mehdi Omidi: Tarbiat Modares University
Mohsen Mohammadzadeh: Tarbiat Modares University

Statistical Papers, 2016, vol. 57, issue 3, No 7, 689-703

Abstract: 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.

Keywords: Copula function; Spatio-temporal covariance; Genetic algorithm (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s00362-015-0674-2

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