An Overview of Kriging and Cokriging Predictors for Functional Random Fields

Ramón Giraldo, Víctor Leiva () and Cecilia Castro
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Ramón Giraldo: Departamento de Estadística, Universidad Nacional de Colombia, Sede Bogotá, Bogotá 111321, Colombia
Víctor Leiva: School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile
Cecilia Castro: Centre of Mathematics, Universidade do Minho, 4710-057 Braga, Portugal

Mathematics, 2023, vol. 11, issue 15, 1-22

Abstract: 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 theoretical 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.

Keywords: functional data; geostatistics; kriging; non-stationarity; spatial prediction; stationarity (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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