Abstract
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 language | English (US) |
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| Pages (from-to) | 85-106 |
| Number of pages | 22 |
| Journal | Journal of the Royal Statistical Society. Series B: Statistical Methodology |
| Volume | 77 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2015 |
| Externally published | Yes |