1 citations · 1 across the 5 of their papers we have counts for
5 papers
Bayesian models for missing and misclassified variables using integrated nested Laplace approximations
Emma Skarstein, Leonardo Soares Bastos, Håvard Rue +1
Misclassified variables used in regression models, either as a covariate or as the response, may lead to biased estimators and incorrect inference. Even though Bayesian models to a…
inlamemi: An R package for missing data imputation and measurement error modelling using INLA
Emma Skarstein, Stefanie Muff
Measurement error and missing data in variables used in statistical models are common, and can at worst lead to serious biases in analyses if they are ignored. Yet, these problems…
The Point Process Framework for Integrated Modelling of Biodiversity Data
Kwaku Peprah Adjei, Philip Mostert, Jorge Sicacha Parada +2
The quantity and types of biodiversity data being collected have increased in recent years. If we are to model and monitor biodiversity effectively, we need to respect how differen…
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…
How is model-related uncertainty quantified and reported in different disciplines?
Emily G. Simmonds, Kwaku Peprah Adjei, Christoffer Wold Andersen +40
How do we know how much we know? Quantifying uncertainty associated with our modelling work is the only way we can answer how much we know about any phenomenon. With quantitative s…