Dimension-Reduced Modeling of Spatio-Temporal Processes
The field of spatial and spatio-temporal statistics is increasingly faced with the challenge of very large datasets. The classical approach to spatial and spatio-temporal modeling is very computationally demanding when datasets are large, which has led to interest in methods that use dimension-reduc...
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ftdatacite:10.6084/m9.figshare.1276462 2023-05-15T13:43:59+02:00 Dimension-Reduced Modeling of Spatio-Temporal Processes Brynjarsdóttir, Jenný L. Mark Berliner 2014 https://dx.doi.org/10.6084/m9.figshare.1276462 https://tandf.figshare.com/articles/dataset/Dimension_Reduced_Modeling_of_Spatio_Temporal_Processes/1276462 unknown Taylor & Francis https://dx.doi.org/10.1080/01621459.2014.904232 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY 110309 Infectious Diseases FOS Health sciences Cancer Mathematics FOS Mathematics Information and Computing Sciences Biological Sciences Ecology FOS Biological sciences Earth and Environmental Sciences dataset Dataset 2014 ftdatacite https://doi.org/10.6084/m9.figshare.1276462 https://doi.org/10.1080/01621459.2014.904232 2021-11-05T12:55:41Z The field of spatial and spatio-temporal statistics is increasingly faced with the challenge of very large datasets. The classical approach to spatial and spatio-temporal modeling is very computationally demanding when datasets are large, which has led to interest in methods that use dimension-reduction techniques. In this article, we focus on modeling of two spatio-temporal processes where the primary goal is to predict one process from the other and where datasets for both processes are large. We outline a general dimension-reduced Bayesian hierarchical modeling approach where spatial structures of both processes are modeled in terms of a low number of basis vectors, hence reducing the spatial dimension of the problem. Temporal evolution of the processes and their dependence is then modeled through the coefficients of the basis vectors. We present a new method of obtaining data-dependent basis vectors, which is geared toward the goal of predicting one process from the other. We apply these methods to a statistical downscaling example, where surface temperatures on a coarse grid over Antarctica are downscaled onto a finer grid. Supplementary materials for this article are available online. Dataset Antarc* Antarctica DataCite Metadata Store (German National Library of Science and Technology) |
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Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
110309 Infectious Diseases FOS Health sciences Cancer Mathematics FOS Mathematics Information and Computing Sciences Biological Sciences Ecology FOS Biological sciences Earth and Environmental Sciences |
spellingShingle |
110309 Infectious Diseases FOS Health sciences Cancer Mathematics FOS Mathematics Information and Computing Sciences Biological Sciences Ecology FOS Biological sciences Earth and Environmental Sciences Brynjarsdóttir, Jenný L. Mark Berliner Dimension-Reduced Modeling of Spatio-Temporal Processes |
topic_facet |
110309 Infectious Diseases FOS Health sciences Cancer Mathematics FOS Mathematics Information and Computing Sciences Biological Sciences Ecology FOS Biological sciences Earth and Environmental Sciences |
description |
The field of spatial and spatio-temporal statistics is increasingly faced with the challenge of very large datasets. The classical approach to spatial and spatio-temporal modeling is very computationally demanding when datasets are large, which has led to interest in methods that use dimension-reduction techniques. In this article, we focus on modeling of two spatio-temporal processes where the primary goal is to predict one process from the other and where datasets for both processes are large. We outline a general dimension-reduced Bayesian hierarchical modeling approach where spatial structures of both processes are modeled in terms of a low number of basis vectors, hence reducing the spatial dimension of the problem. Temporal evolution of the processes and their dependence is then modeled through the coefficients of the basis vectors. We present a new method of obtaining data-dependent basis vectors, which is geared toward the goal of predicting one process from the other. We apply these methods to a statistical downscaling example, where surface temperatures on a coarse grid over Antarctica are downscaled onto a finer grid. Supplementary materials for this article are available online. |
format |
Dataset |
author |
Brynjarsdóttir, Jenný L. Mark Berliner |
author_facet |
Brynjarsdóttir, Jenný L. Mark Berliner |
author_sort |
Brynjarsdóttir, Jenný |
title |
Dimension-Reduced Modeling of Spatio-Temporal Processes |
title_short |
Dimension-Reduced Modeling of Spatio-Temporal Processes |
title_full |
Dimension-Reduced Modeling of Spatio-Temporal Processes |
title_fullStr |
Dimension-Reduced Modeling of Spatio-Temporal Processes |
title_full_unstemmed |
Dimension-Reduced Modeling of Spatio-Temporal Processes |
title_sort |
dimension-reduced modeling of spatio-temporal processes |
publisher |
Taylor & Francis |
publishDate |
2014 |
url |
https://dx.doi.org/10.6084/m9.figshare.1276462 https://tandf.figshare.com/articles/dataset/Dimension_Reduced_Modeling_of_Spatio_Temporal_Processes/1276462 |
genre |
Antarc* Antarctica |
genre_facet |
Antarc* Antarctica |
op_relation |
https://dx.doi.org/10.1080/01621459.2014.904232 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.1276462 https://doi.org/10.1080/01621459.2014.904232 |
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1766195639137337344 |