Cokriging Prediction Using as Secondary Variable a Functional Random Field with Application in Environmental Pollution

Ramón Giraldo, Luis Herrera and Víctor Leiva
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Ramón Giraldo: Department of Statistics, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Luis Herrera: Department of Statistics, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Víctor Leiva: School of Industrial Engineering, Pontificia Universidad Católica de Valparaíso, Valparaíso 2362807, Chile

Mathematics, 2020, vol. 8, issue 8, 1-13

Abstract: Cokriging is a geostatistical technique that is used for spatial prediction when realizations of a random field are available. If a secondary variable is cross-correlated with the primary variable, both variables may be employed for prediction by means of cokriging. In this work, we propose a predictive model that is based on cokriging when the secondary variable is functional. As in the ordinary cokriging, a co-regionalized linear model is needed in order to estimate the corresponding auto-correlations and cross-correlations. The proposed model is utilized for predicting the environmental pollution of particulate matter when considering wind speed curves as functional secondary variable.

Keywords: functional data analysis; functional random fields; geostatistics; kriging; R software (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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