Likelihood-free simulation-based optimal design with an application to spatial extremes

In this paper we employ a novel method to find the optimal design for problems where the likelihood is not available analytically, but simulation from the likelihood is feasible. To approximate the expected utility we make use of approximate Bayesian computation methods. We detail the approach for a...

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Detalles Bibliográficos
Autores principales: Hainy, Markus, Müller, Werner G., Wagner, Helga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2015
Materias:
Original Paper
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981187/
https://www.ncbi.nlm.nih.gov/pubmed/27563280
http://dx.doi.org/10.1007/s00477-015-1067-8
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author Hainy, Markus
Müller, Werner G.
Wagner, Helga
author_facet Hainy, Markus
Müller, Werner G.
Wagner, Helga
author_sort Hainy, Markus
collection PubMed
description In this paper we employ a novel method to find the optimal design for problems where the likelihood is not available analytically, but simulation from the likelihood is feasible. To approximate the expected utility we make use of approximate Bayesian computation methods. We detail the approach for a model on spatial extremes, where the goal is to find the optimal design for efficiently estimating the parameters determining the dependence structure. The method is applied to determine the optimal design of weather stations for modeling maximum annual summer temperatures. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00477-015-1067-8) contains supplementary material, which is available to authorized users.
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spelling pubmed-49811872016-08-23 Likelihood-free simulation-based optimal design with an application to spatial extremes Hainy, Markus Müller, Werner G. Wagner, Helga Stoch Environ Res Risk Assess Original Paper In this paper we employ a novel method to find the optimal design for problems where the likelihood is not available analytically, but simulation from the likelihood is feasible. To approximate the expected utility we make use of approximate Bayesian computation methods. We detail the approach for a model on spatial extremes, where the goal is to find the optimal design for efficiently estimating the parameters determining the dependence structure. The method is applied to determine the optimal design of weather stations for modeling maximum annual summer temperatures. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s00477-015-1067-8) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2015-04-12 2016 /pmc/articles/PMC4981187/ /pubmed/27563280 http://dx.doi.org/10.1007/s00477-015-1067-8 Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Paper
Hainy, Markus
Müller, Werner G.
Wagner, Helga
Likelihood-free simulation-based optimal design with an application to spatial extremes
title Likelihood-free simulation-based optimal design with an application to spatial extremes
title_full Likelihood-free simulation-based optimal design with an application to spatial extremes
title_fullStr Likelihood-free simulation-based optimal design with an application to spatial extremes
title_full_unstemmed Likelihood-free simulation-based optimal design with an application to spatial extremes
title_short Likelihood-free simulation-based optimal design with an application to spatial extremes
title_sort likelihood-free simulation-based optimal design with an application to spatial extremes
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4981187/
https://www.ncbi.nlm.nih.gov/pubmed/27563280
http://dx.doi.org/10.1007/s00477-015-1067-8
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