Excursion and contour uncertainty regions for latent Gaussian models
Artikel i vetenskaplig tidskrift, 2015

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.

Excursion sets

Latent Gaussian models

Multiple testing

Contour curves


David Bolin

Chalmers, Matematiska vetenskaper, matematisk statistik

Göteborgs universitet

Finn Lindgren

University of Bath

Journal of the Royal Statistical Society. Series B: Statistical Methodology

1369-7412 (ISSN) 1467-9868 (eISSN)

Vol. 77 85-106


Sannolikhetsteori och statistik