35 citations · 65 across the 9 of their papers we have counts for
10 papers
Cross Validation for the log Gaussian Cox Process
Hans Montcho, Håvard Rue, Finn Lindgren +4
The log Gaussian Cox Process (LGCP) is one of the most widely used models for the analysis of spatial point patterns. Although Bayesian methods and software for fitting LGCPs are n…
Fast spatial simulation of extreme high-resolution radar precipitation data using INLA
Silius M. Vandeskog, Raphaël Huser, Oddbjørn Bruland +1
Aiming to deliver improved precipitation simulations for hydrological impact assessment studies, we develop a methodology for modelling and simulating high-dimensional spatial prec…
A joint Bayesian framework for missing data and measurement error using integrated nested Laplace approximations
Emma Sofie Skarstein, Sara Martino, Stefanie Muff
Measurement error (ME) and missing values in covariates are often unavoidable in disciplines that deal with data, and both problems have separately received considerable attention…
A spatio-temporal analysis of NO concentrations during the Italian 2020 COVID-19 lockdown
Guido Fioravanti, Michela Cameletti, Sara Martino +2
When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify - in space and time - the effectiveness…
Modelling sub-daily precipitation extremes with the blended generalised extreme value distribution
Silius M. Vandeskog, Sara Martino, Daniela Castro-Camilo +1
A new method is proposed for modelling the yearly maxima of sub-daily precipitation, with the aim of producing spatial maps of return level estimates. Yearly precipitation maxima a…
Integration of presence-only data from several sources. A case study on dolphins' spatial distribution
Sara Martino, Daniela Silvia Pace, Stefano Moro +8
Presence-only data are a typical occurrence in species distribution modeling. They include the presence locations and no information on the absence. Their modeling usually does not…
Importance Sampling with the Integrated Nested Laplace Approximation
Martin Outzen Berild, Sara Martino, Virgilio Gómez-Rubio +1
The Integrated Nested Laplace Approximation (INLA) is a deterministic approach to Bayesian inference on latent Gaussian models (LGMs) and focuses on fast and accurate approximation…
Spatio-temporal modelling of daily concentrations in Italy using the SPDE approach
Guido Fioravanti, Sara Martino, Michela Cameletti +1
This paper illustrates the main results of a spatio-temporal interpolation process of concentrations at daily resolution using a set of 410 monitoring sites, distr…
Integrated Nested Laplace Approximations (INLA)
Sara Martino, Andrea Riebler
This is a short description and basic introduction to the Integrated nested Laplace approximations (INLA) approach. INLA is a deterministic paradigm for Bayesian inference in laten…
Estimation of a non-stationary model for annual precipitation in southern Norway using replicates of the spatial field
Rikke Ingebrigtsen, Finn Lindgren, Ingelin Steinsland +1
Estimation of stationary dependence structure parameters using only a single realisation of the spatial process, typically leads to inaccurate estimates and poorly identified param…