Cokriging prediction using as secondary variable a functional random field with application in environmental pollution

Ramón Giraldo, Luis Herrera, VICTOR ELISEO LEIVA SANCHEZ

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

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.

Original languageEnglish
Article number1305
JournalMathematics
Volume8
Issue number8
DOIs
StatePublished - Aug 2020
Externally publishedYes

Keywords

  • Functional data analysis
  • Functional random fields
  • Geostatistics
  • Kriging
  • R software

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