Open AccessCOMPARISON OF EXPONENTIAL COVARIANCE FUNCTIONS FOR BIVARIATE GEOSTATISTICAL DATA
Author(s) -
Angélica Maria Tortola Ribeiro,
Paulo Justiniano Ribeiro Júnior,
Wagner Hugo Bonat
Publication year - 2021
Publication title -
revista de matemática e estatística
Language(s) - English
Resource type - Journals
eISSN - 1980-4245
pISSN - 0102-0811
DOI - 10.28951/rbb.v39i1.558
Subject(s) - bivariate analysis , covariance , mathematics , exponential function , bivariate data , covariance function , statistics , covariance mapping , econometrics , covariance intersection , mathematical analysis
In the analysis of multivariate spatial random elds, it is essential to dene a covariance structure that adequately models the relationship between the variables under study. We propose a covariance structure with exponential correlation function for bivariate random elds, the SEC model. We compare the SEC model fits with the bivariate separable exponential model and the bivariate exponential model with constraints, which are particular cases of the full bivariate Matern model, presented in the literature. A simulation study assess characteristics of the proposed model. The model is tted to a weather data set from Brazil, bearing in mind the importance of analyzing climate data to predict adverse environmental conditions. Predictive measures are used to compare the models under study. The satisfactory results compared to the models considered and the simpler structure makes the SEC model an alternative for the analysis of bivariate spatial elds.