| Title |
Kriging model with fractional euclidean distance matrices / |
| Authors |
Pozniak, Natalija ; Sakalauskas, Leonidas ; Saltyte, Laura |
| DOI |
10.15388/Informatica.2019.210 |
| Full Text |
|
| Is Part of |
Informatica.. Vilnius : Vilniaus universitetas. 2019, vol. 30, no. 2, p. 367-390.. ISSN 0868-4952. eISSN 1822-8844 |
| Keywords [eng] |
scattered data ; fractional Euclidean distance matrices ; multivariate normal distribution ; homogeneous Gaussian field ; maximum likelihood |
| Abstract [eng] |
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 themaximumlikelihoodmethod. 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. |
| Published |
Vilnius : Vilniaus universitetas |
| Type |
Journal article |
| Language |
English |
| Publication date |
2019 |
| CC license |
|
