Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/86362
Título: | An overview of kriging and cokriging predictors for functional random fields |
Autor(es): | Giraldo, Ramón Leiva, Víctor Castro, Cecília |
Palavras-chave: | Functional data Geostatistics Kriging Non-stationarity Spatial prediction Stationarity |
Data: | 7-Ago-2023 |
Editora: | Multidisciplinary Digital Publishing Institute (MDPI) |
Revista: | Mathematics |
Resumo(s): | This article presents an overview of methodologies for spatial prediction of functional data, focusing on both stationary and non-stationary conditions. A significant aspect of the functional random fields analysis is evaluating stationarity to characterize the stability of statistical properties across the spatial domain. The article explores methodologies from the literature, providing insights into the challenges and advancements in functional geostatistics. This work is relevant from theoreti cal and practical perspectives, offering an integrated view of methodologies tailored to the specific stationarity conditions of the functional processes under study. The practical implications of our work span across fields like environmental monitoring, geosciences, and biomedical research. This overview encourages advancements in functional geostatistics, paving the way for the development of innovative techniques for analyzing and predicting spatially correlated functional data. It lays the groundwork for future research, enhancing our understanding of spatial statistics and its applications. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/86362 |
DOI: | 10.3390/math11153425 |
ISSN: | 2227-7390 |
Versão da editora: | https://www.mdpi.com/2227-7390/11/15/3425 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CMAT - Artigos em revistas com arbitragem / Papers in peer review journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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mathematics-11-03425.pdf | 410,14 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons