A new local stochastic method for predicting data with spatial heterogeneity

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...

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Bibliographic Details
Main Authors:	Anderson Rodrigo da Silva, Ana Paula Alencastro Silva, Lauro Joaquim Tiago Neto
Format:	Article
Language:	English
Published:	Eduem (Editora da Universidade Estadual de Maringá) 2020-11-01
Series:	Acta Scientiarum: Agronomy
Subjects:	moving window kriging; spatial prediction; soil nematodes.
Online Access:	https://periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/49947

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