Excursion and contour uncertainty regions for latent Gaussian models

David Bolin, Finn Lindgren

Research output: Contribution to journalArticlepeer-review

85 Citations (SciVal)
242 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)85-106
JournalJournal of the Royal Statistical Society, Series B (Statistical Methodology)
Issue number1
Early online date17 Mar 2014
Publication statusPublished - Jan 2015


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