Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach
Environmental processes, including climatic impacts in cold regions, are typically acting at multiple spatial and temporal scales. Hierarchical models are a flexible statistical tool that allows for decomposing spatiotemporal processes in simpler components connected by conditional probabilistic rel...
Published in: | Arctic, Antarctic, and Alpine Research |
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Format: | Article in Journal/Newspaper |
Language: | English |
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Taylor & Francis Group
2019
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Online Access: | https://doi.org/10.1080/15230430.2019.1585175 https://doaj.org/article/3821d677ab184b0582e805863403a02f |
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fttriple:oai:gotriple.eu:oai:doaj.org/article:3821d677ab184b0582e805863403a02f 2023-05-15T14:14:25+02:00 Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach Michela Cameletti Franco Biondi 2019-01-01 https://doi.org/10.1080/15230430.2019.1585175 https://doaj.org/article/3821d677ab184b0582e805863403a02f en eng Taylor & Francis Group 1523-0430 1938-4246 doi:10.1080/15230430.2019.1585175 https://doaj.org/article/3821d677ab184b0582e805863403a02f undefined Arctic, Antarctic, and Alpine Research, Vol 51, Iss 1, Pp 115-127 (2019) tree rings stem barcast climate reconstruction autocorrelation geo envir Journal Article https://vocabularies.coar-repositories.org/resource_types/c_6501/ 2019 fttriple https://doi.org/10.1080/15230430.2019.1585175 2023-01-22T17:53:11Z Environmental processes, including climatic impacts in cold regions, are typically acting at multiple spatial and temporal scales. Hierarchical models are a flexible statistical tool that allows for decomposing spatiotemporal processes in simpler components connected by conditional probabilistic relationships. This article reviews two hierarchical models that have been applied to tree-ring proxy records of climate to model their space–time structure: STEM (Spatio-Temporal Expectation Maximization) and BARCAST (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time). Both models account for spatial and temporal autocorrelation by including latent spatiotemporal processes, and they both take into consideration measurement and model errors, while they differ in their inferential approach. STEM adopts the frequentist perspective, and its parameters are estimated through the expectation-maximization (EM) algorithm, with uncertainty assessed through bootstrap resampling. BARCAST is developed in the Bayesian framework, and relies on Markov chain Monte Carlo (MCMC) algorithms for sampling values from posterior probability distributions of interest. STEM also explicitly includes covariates in the process model definition. As hierarchical modeling keeps contributing to the analysis of complex ecological and environmental processes, proxy reconstructions are likely to improve, thereby providing better constraints on future climate change scenarios and their impacts over cold regions. Article in Journal/Newspaper Antarctic and Alpine Research Arctic Unknown Arctic, Antarctic, and Alpine Research 51 1 115 127 |
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Open Polar |
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language |
English |
topic |
tree rings stem barcast climate reconstruction autocorrelation geo envir |
spellingShingle |
tree rings stem barcast climate reconstruction autocorrelation geo envir Michela Cameletti Franco Biondi Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach |
topic_facet |
tree rings stem barcast climate reconstruction autocorrelation geo envir |
description |
Environmental processes, including climatic impacts in cold regions, are typically acting at multiple spatial and temporal scales. Hierarchical models are a flexible statistical tool that allows for decomposing spatiotemporal processes in simpler components connected by conditional probabilistic relationships. This article reviews two hierarchical models that have been applied to tree-ring proxy records of climate to model their space–time structure: STEM (Spatio-Temporal Expectation Maximization) and BARCAST (Bayesian Algorithm for Reconstructing Climate Anomalies in Space and Time). Both models account for spatial and temporal autocorrelation by including latent spatiotemporal processes, and they both take into consideration measurement and model errors, while they differ in their inferential approach. STEM adopts the frequentist perspective, and its parameters are estimated through the expectation-maximization (EM) algorithm, with uncertainty assessed through bootstrap resampling. BARCAST is developed in the Bayesian framework, and relies on Markov chain Monte Carlo (MCMC) algorithms for sampling values from posterior probability distributions of interest. STEM also explicitly includes covariates in the process model definition. As hierarchical modeling keeps contributing to the analysis of complex ecological and environmental processes, proxy reconstructions are likely to improve, thereby providing better constraints on future climate change scenarios and their impacts over cold regions. |
format |
Article in Journal/Newspaper |
author |
Michela Cameletti Franco Biondi |
author_facet |
Michela Cameletti Franco Biondi |
author_sort |
Michela Cameletti |
title |
Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach |
title_short |
Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach |
title_full |
Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach |
title_fullStr |
Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach |
title_full_unstemmed |
Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach |
title_sort |
hierarchical modeling of space-time dendroclimatic fields: comparing a frequentist and a bayesian approach |
publisher |
Taylor & Francis Group |
publishDate |
2019 |
url |
https://doi.org/10.1080/15230430.2019.1585175 https://doaj.org/article/3821d677ab184b0582e805863403a02f |
genre |
Antarctic and Alpine Research Arctic |
genre_facet |
Antarctic and Alpine Research Arctic |
op_source |
Arctic, Antarctic, and Alpine Research, Vol 51, Iss 1, Pp 115-127 (2019) |
op_relation |
1523-0430 1938-4246 doi:10.1080/15230430.2019.1585175 https://doaj.org/article/3821d677ab184b0582e805863403a02f |
op_rights |
undefined |
op_doi |
https://doi.org/10.1080/15230430.2019.1585175 |
container_title |
Arctic, Antarctic, and Alpine Research |
container_volume |
51 |
container_issue |
1 |
container_start_page |
115 |
op_container_end_page |
127 |
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1766286887483342848 |