A new local stochastic method for predicting data with spatial heterogeneity
Spatial data (e.g., phytopathogenic data) do not always meet assumptions such as stationarity, isotropy and Gaussian distribution, thereby requiring complex spatial methods and models. Some deterministic assumption-free methods such as the inverse distance weighting can also be applied to predict sp...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Eduem (Editora da Universidade Estadual de Maringá)
2020-11-01
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| Series: | Acta Scientiarum: Agronomy |
| Subjects: | |
| Online Access: | https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947 |