Spectral adjustment for spatial confounding.

Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. We derive necessary conditions on the coherence between the exposure and the unmeasured confounder that ensure the effect of exposure is estimable. We specify our model and assumptions in the spectral domain to allow for different degrees of confounding at different spatial resolutions.

Variance partitioning in spatio-temporal disease mapping models.

Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect precision parameters. We introduce a reparametrized version of the popular spatio-temporal interaction models, based on Kronecker product intrinsic Gaussian Markov random fields, that we name the variance partitioning model. The variance partitioning model includes a mixing parameter that balances the contribution of the main and interaction effects to the total (generalized) variance and enhances interpretability.

Using invertebrate functional traits to improve flow variability assessment within European rivers.

Rivers are among the most threatened ecosystems worldwide and are experiencing rapid biodiversity loss. Flow alteration due to climate change, water abstraction and augmentation is a severe stressor on many aquatic communities. Macroinvertebrates are widely used for biomonitoring river ecosystems although current taxonomic approaches used to characterise ecological responses to flow have limitations in terms of generalisation across biogeographical regions.

A note on intrinsic conditional autoregressive models for disconnected graphs.

In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.

Quasi-periodic spatiotemporal models of brain activation in single-trial MEG experiments.

Magneto-encephalography (MEG) is an imaging technique which measures neuronal activity in the brain. Even when a subject is in a resting state, MEG data show characteristic spatial and temporal patterns, resulting from electrical current at specific locations in the brain. The key pattern of interest is a 'dipole', consisting of two adjacent regions of high and low activation which oscillate over time in an out-of-phase manner.