### Notice

 dc.contributor.author Qadir, Ghulam A. dc.contributor.author Sun, Ying dc.date.accessioned 2019-12-18T12:03:39Z dc.date.available 2019-12-18T12:03:39Z dc.date.issued 2019-11-06 dc.identifier.uri http://hdl.handle.net/10754/660675.1 dc.description.abstract The prevalence of spatially referenced multivariate data has impelled researchers to develop a procedure for the joint modeling of multiple spatial processes. This ordinarily involves modeling marginal and cross-process dependence for any arbitrary pair of locations using a multivariate spatial covariance function. However, building a flexible multivariate spatial covariance function that is nonnegative definite is challenging. Here, we propose a semiparametric approach for multivariate spatial covariance function estimation with approximate Mat\'ern marginals and highly flexible cross-covariance functions via their spectral representations. The flexibility in our cross-covariance function arises due to B-spline based specification of the underlying coherence functions, which in turn allows us to capture non-trivial cross-spectral features. We then develop a likelihood-based estimation procedure and perform multiple simulation studies to demonstrate the performance of our method, especially on the coherence function estimation. Finally, we analyze particulate matter concentrations ($\text{PM}_{2.5}$) and wind speed data over the North-Eastern region of the United States, where we illustrate that our proposed method outperforms the commonly used full bivariate Mat\'ern model and the linear model of coregionalization for spatial prediction. dc.publisher arXiv dc.relation.url https://arxiv.org/pdf/1911.02258 dc.rights Archived with thanks to arXiv dc.title Semiparametric Estimation of Cross-covariance Functions for Multivariate Random Fields dc.type Preprint dc.contributor.department CEMSE Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. dc.contributor.department Statistics Program dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.eprint.version Pre-print dc.identifier.arxivid 1911.02258 kaust.person Qadir, Ghulam A. kaust.person Sun, Ying refterms.dateFOA 2019-12-18T12:04:22Z
﻿

### Files in this item

Name:
Preprintfile1.pdf
Size:
1.942Mb
Format:
PDF
Description:
Pre-print

### This item appears in the following Collection(s)

VersionItemEditorDateSummary

*Selected version