Time varying spatio-temporal covariance models

Ryan H.L. IP, Wai Keung LI

Research output: Contribution to journalArticlespeer-review

6 Citations (Scopus)

Abstract

In this paper, we introduce valid parametric covariance models for univariate and multivariate spatio-temporal random fields. In contrast to the traditional models, we allow the model parameters to vary over time. Since variables in applications usually exhibit seasonality or changes in dependency structures, the allowance of varying parameters would be beneficial in terms of improving model flexibility. Conditions in constructing valid covariance models and discussions on practical implementation will be provided. As an application, a set of air pollution data observed from a monitoring network will be modeled. It is found that the time varying model performs better in prediction compared with the traditional models. Copyright © 2015 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)269-285
JournalSpatial Statistics
Volume14
Issue numberPart C
Early online dateJul 2015
DOIs
Publication statusPublished - Nov 2015

Citation

Ip, R. H. L., & Li, W. K. (2015). Time varying spatio-temporal covariance models. Spatial Statistics, 14(Part C), 269-285. doi: 10.1016/j.spasta.2015.06.006

Keywords

  • Valid covariance models
  • Multivariate processes
  • Prediction
  • Monitoring datasets

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