Modeling Temporally Evolving and Spatially Globally Dependent Data-Reference-Cited by-同舟云学术

Modeling Temporally Evolving and Spatially Globally Dependent Data

Published:2018-05-24 Issue:2 Volume:86 Page:344-377
ISSN:0306-7734
Container-title:International Statistical Review
language:en
Short-container-title:International Statistical Review

Author:

Porcu Emilio¹^ORCID,Alegria Alfredo²,Furrer Reinhard³^ORCID

Affiliation:

1. School of Mathematics, Statistics and Physics University of Newcastle, Newcastle Upon Tyne, England, and Department of Mathematics, University Federico Santa Maria Valparaiso 2360102 Chile

2. School of Mathematics, Statistics and Physics University of Newcastle Newcastle Upon Tyne England

3. Department of Mathematics and Department of Computational Science University of Zurich 8057 Zurich Switzerland

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/insr.12266

Reference127 articles.

1. Space-time models for moving fields with an application to significant wave height fields

2. Alegria A.&Porcu E.(2016).Space‐Time Geostatistical Models with both Linear and Seasonal Structures in the Temporal Components. Technical Report University Federico Santa Maria Available at arXiv:1702.01400.

3. The dimple problem related to space–time modeling under the Lagrangian framework

4. Alegria A. Porcu E. Furrer R.&Mateu J.(2017).Covariance Functions for Multivariate Gaussian Fields Evolving Temporally over Planet Earth. Technical Report University Federico Santa Maria.

5. Multivariate and multiradial Schoenberg measures with their dimension walks

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