Affiliations: [1] Institute of Data Science and Digital Technologies of Vilnius University, Akademijos str. 4, LT04812 Vilnius, Lithuania | [2] Klaipeda University, H. Manto str. 84, Klaipeda, Lithuania. E-mails: [email protected], [email protected], [email protected]
Correspondence:
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Corresponding author.
Abstract: The multidimensional data model for kriging is developed using fractional Euclidean distance matrices (FEDM). The properties of FEDM are studied by means of the kernel matrix mehod. It has been shown that the factorization of kernel matrix enables us to create the embedded set being a nonsingular simplex. Using the properties of FEDM the Gaussian random field (GRF) is constructed doing it without positive definite correlation functions usually applied for such a purpose. Created GRF can be considered as a multidimensional analogue of the Wiener process, for instance, line realizations of this GRF are namely Wiener processes. Next, the kriging method is developed based on FEDM. The method is rather simple and depends on parameters that are simply estimated by the maximum likelihood method. Computer simulation of the developed kriging extrapolator has shown that it outperforms the well known Shepard inverse distance extrapolator. Practical application of the developed approach to surrogate modelling of wastewater treatment is discussed. Theoretical investigation, computer simulation, and a practical example demonstrate that the proposed kriging model, using FEDM, can be efficiently applied to multidimensional data modelling and processing.
Keywords: scattered data, fractional Euclidean distance matrices, multivariate normal distribution, homogeneous Gaussian field, maximum likelihood
Journal: Informatica, vol. 30, no. 2, pp. 367-390, 2019
