Excursion and contour uncertainty regions for latent Gaussian models

TL;DR

This paper introduces a new method for estimating uncertainty regions in latent Gaussian models, enabling accurate identification of areas exceeding certain levels in various applications like environmental monitoring.

Contribution

It proposes a novel approach combining parametric excursion set families with sequential importance sampling for joint probability estimation in latent Gaussian models.

Findings

Method accurately estimates pollution exceedance regions in Italy.

Method identifies vegetation increase regions in the African Sahel.

Validated with simulated data and real environmental applications.

Abstract

An interesting statistical problem is to find regions where some studied process 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 that occurs in several areas of applications ranging from brain imaging to astrophysics. In this work, a method for solving this problem, as well as the related problem of finding uncertainty 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 using simulated data and two environmental applications are presented. In the first application, areas where the air pollution in the Piemonte region in northern Italy exceeds the daily limit value, set by the European Union for human health protection, are estimated. In the second application, regions in the African Sahel that experienced an increase in vegetation after the drought period in the early 1980s are estimated.