A new mixture copula model for spatially correlated multiple variables with an environmental application.

ABSTRACT: In environmental monitoring, multiple spatial variables are often sampled at a geographical location that can depend on each other in complex ways, such as non-linear and non-Gaussian spatial dependence. We propose a new mixture copula model that can capture those complex relationships of spatially correlated multiple variables and predict univariate variables while considering the multivariate spatial relationship. The proposed method is demonstrated using an environmental application and compared with three existing methods. Firstly, improvement in the prediction of individual variables by utilising multivariate spatial copula compares to the existing univariate pair copula method. Secondly, performance in prediction by utilising mixture copula in the multivariate spatial copula framewor

SUBMITTER: Abraj M 

PROVIDER: S-EPMC9381801 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature