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Excursion and contour uncertainty regions for latent Gaussian models

David Bolin (Institutionen för matematiska vetenskaper, matematisk statistik) ; Finn Lindgren
Journal of The Royal Statistical Society Series B-statistical Methodology (1369-7412). Vol. 77 (2015), 1, p. 85-106.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Contour curves, Excursion sets, Latent Gaussian models, Multiple testing

Denna post skapades 2014-03-18. Senast ändrad 2015-02-26.
CPL Pubid: 195183


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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)


Matematisk statistik

Chalmers infrastruktur