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Cited by 96

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Excursion and Contour Uncertainty Regions for Latent Gaussian Models

Bolin, David, Lindgren, Finn·2015·Journal of the Royal Statistical Society Series B: Statistical Methodology
DOI 10.1111/rssb.12055
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Abstract

Summary: In several areas of application ranging from brain imaging to astrophysics and geostatistics, an important statistical problem is to find regions where the process studied exceeds a certain level. Estimating such regions so that the probability for exceeding the level in the entire set is equal to some predefined value is a difficult problem connected to the problem of multiple significance testing. In this work, a method for solving this problem, as well as the related problem of finding credible regions for contour curves, for latent Gaussian models is proposed. The method is based on using a parametric family for the excursion sets in combination with a sequential importance sampling method for estimating joint probabilities. The accuracy of the method is investigated by using simulated data and an environmental application is presented.

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Metadata

Authors
Bolin, D., & Lindgren, F.
Affiliations
  • Chalmers University of Technology , Gothenburg , Sweden
  • University of Bath , UK
Venue
Journal of the Royal Statistical Society Series B: Statistical Methodology
Publisher
Oxford University Press (OUP)
Published
2014-03-17
Volume
77
Issue
1
Pages
85-106
ISSN
  • 1369-7412
  • 1467-9868
DOI
10.1111/rssb.12055
Cited by
96
References
3
Language
en
Licence
academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)

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CoChat. (2026). Citation check: Excursion and Contour Uncertainty Regions for Latent Gaussian Models. https://app.cochat.ai/papers/10.1111/rssb.12055
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Bolin, D., & Lindgren, F. (2015). Excursion and Contour Uncertainty Regions for Latent Gaussian Models. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/rssb.12055

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