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Efficient inference of generalized spatial fusion models with flexible specification


Wang, Craig; Furrer, Reinhard (2019). Efficient inference of generalized spatial fusion models with flexible specification. Stat, 8(1):e216.

Abstract

In spatial statistics, data are often collected at different spatial resolutions. Often, it is of interest to (a) carry out multivariate analysis when variables are sampled at different locations, (b) model data collected at misaligned areas, or (c) unravel common latent factors by jointly modelling point and areal data. In this paper, we establish a linkage between the generalized spatial fusion model framework and the various change‐of‐support problems, and we outline how the framework can be adapted in these situations. Moreover, we propose an efficient fusion model implementation by exploiting advantages of nearest neighbour Gaussian process and the Stan modelling language. Our simulation shows that the computational efficiency is several times higher in the new implementation compared with original implementation. We illustrate the performance gain in practice using a case study, which models daily precipitation in Switzerland based on rain gauge and radar data.

Abstract

In spatial statistics, data are often collected at different spatial resolutions. Often, it is of interest to (a) carry out multivariate analysis when variables are sampled at different locations, (b) model data collected at misaligned areas, or (c) unravel common latent factors by jointly modelling point and areal data. In this paper, we establish a linkage between the generalized spatial fusion model framework and the various change‐of‐support problems, and we outline how the framework can be adapted in these situations. Moreover, we propose an efficient fusion model implementation by exploiting advantages of nearest neighbour Gaussian process and the Stan modelling language. Our simulation shows that the computational efficiency is several times higher in the new implementation compared with original implementation. We illustrate the performance gain in practice using a case study, which models daily precipitation in Switzerland based on rain gauge and radar data.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
07 Faculty of Science > Institute for Computational Science
Dewey Decimal Classification:510 Mathematics
Scopus Subject Areas:Physical Sciences > Statistics and Probability
Social Sciences & Humanities > Statistics, Probability and Uncertainty
Language:English
Date:1 January 2019
Deposited On:28 Jun 2019 12:50
Last Modified:29 Jul 2020 10:39
Publisher:Wiley-Blackwell Publishing, Inc.
ISSN:2049-1573
OA Status:Hybrid
Publisher DOI:https://doi.org/10.1002/sta4.216
Project Information:
  • : FunderSNSF
  • : Grant ID200021_175529
  • : Project TitleDisentangling evidence from huge multivariate space-time data from the earth sciences

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