TY - JOUR
T1 - Excursion and contour uncertainty regions for latent Gaussian models
AU - Bolin, David
AU - Lindgren, Finn
PY - 2015/1
Y1 - 2015/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84896058677&partnerID=8YFLogxK
UR - http://dx.doi.org/10.1111/rssb.12055
U2 - 10.1111/rssb.12055
DO - 10.1111/rssb.12055
M3 - Article
SN - 1369-7412
VL - 77
SP - 85
EP - 106
JO - Journal of the Royal Statistical Society, Series B (Statistical Methodology)
JF - Journal of the Royal Statistical Society, Series B (Statistical Methodology)
IS - 1
ER -