Vecchia Approximations and Optimization for Multivariate Matérn Models

Journal of Data Science

article

Youssef Fahmy¹  Joseph Guinness¹ 

[1] Department of Statistics and Data Science, Cornell University

关键词: Gaussian process;    Fisher scoring;    software;   

DOI  :  10.6339/22-JDS1074

学科分类：土木及结构工程学

来源: JDS

【 摘 要 】

We describe our implementation of the multivariate Matérn model for multivariate spatial datasets, using Vecchia’s approximation and a Fisher scoring optimization algorithm. We consider various pararameterizations for the multivariate Matérn that have been proposed in the literature for ensuring model validity, as well as an unconstrained model. A strength of our study is that the code is tested on many real-world multivariate spatial datasets. We use it to study the effect of ordering and conditioning in Vecchia’s approximation and the restrictions imposed by the various parameterizations. We also consider a model in which co-located nuggets are correlated across components and find that forcing this cross-component nugget correlation to be zero can have a serious impact on the other model parameters, so we suggest allowing cross-component correlation in co-located nugget terms.

【 授权许可】

CC BY