Excursion and contour uncertainty regions for latent Gaussian models

David Bolin, Finn Lindgren

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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
Number of pages22
JournalJournal of the Royal Statistical Society: Statistical Methodology Series B
Volume77
Issue number1
Early online date17 Mar 2014
DOIs
Publication statusPublished - Jan 2015

Fingerprint

Dive into the research topics of 'Excursion and contour uncertainty regions for latent Gaussian models'. Together they form a unique fingerprint.

Cite this