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...

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Bibliographic Details
Published in:Arctic, Antarctic, and Alpine Research
Main Authors: Michela Cameletti, Franco Biondi
Format: Article in Journal/Newspaper
Language:English
Published: Taylor & Francis Group 2019
Subjects:
geo
Online Access:https://doi.org/10.1080/15230430.2019.1585175
https://doaj.org/article/3821d677ab184b0582e805863403a02f
id fttriple:oai:gotriple.eu:oai:doaj.org/article:3821d677ab184b0582e805863403a02f
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spelling 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
institution Open Polar
collection Unknown
op_collection_id fttriple
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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