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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Online Access: | http://hdl.handle.net/10446/138672 https://doi.org/10.1080/15230430.2019.1585175 |
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ftunivbergamo:oai:aisberg.unibg.it:10446/138672 2024-04-21T07:53:01+00:00 Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach Cameletti, Michela Cameletti, Michela Biondi, Franco 2019 text remote http://hdl.handle.net/10446/138672 https://doi.org/10.1080/15230430.2019.1585175 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000486105800009 volume:51 issue:1 firstpage:115 lastpage:127 journal:ARCTIC ANTARCTIC AND ALPINE RESEARCH http://hdl.handle.net/10446/138672 doi:10.1080/15230430.2019.1585175 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075979633 info:eu-repo/semantics/openAccess Tree ring STEM BARCAST climate reconstruction autocorrelation Settore SECS-S/01 - Statistica info:eu-repo/semantics/article 2019 ftunivbergamo https://doi.org/10.1080/15230430.2019.1585175 2024-03-27T16:01:04Z 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 Aisberg - Archivio istituzionale dell'Università di Bergamo Arctic, Antarctic, and Alpine Research 51 1 115 127 |
institution |
Open Polar |
collection |
Aisberg - Archivio istituzionale dell'Università di Bergamo |
op_collection_id |
ftunivbergamo |
language |
English |
topic |
Tree ring STEM BARCAST climate reconstruction autocorrelation Settore SECS-S/01 - Statistica |
spellingShingle |
Tree ring STEM BARCAST climate reconstruction autocorrelation Settore SECS-S/01 - Statistica Cameletti, Michela Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach |
topic_facet |
Tree ring STEM BARCAST climate reconstruction autocorrelation Settore SECS-S/01 - Statistica |
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. |
author2 |
Cameletti, Michela Biondi, Franco |
format |
Article in Journal/Newspaper |
author |
Cameletti, Michela |
author_facet |
Cameletti, Michela |
author_sort |
Cameletti, Michela |
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 |
publishDate |
2019 |
url |
http://hdl.handle.net/10446/138672 https://doi.org/10.1080/15230430.2019.1585175 |
genre |
Antarctic and Alpine Research |
genre_facet |
Antarctic and Alpine Research |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000486105800009 volume:51 issue:1 firstpage:115 lastpage:127 journal:ARCTIC ANTARCTIC AND ALPINE RESEARCH http://hdl.handle.net/10446/138672 doi:10.1080/15230430.2019.1585175 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-85075979633 |
op_rights |
info:eu-repo/semantics/openAccess |
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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1796936287092998144 |