Coming up with Bayesian models for spatial data is easy, but performing inference with them
can be challenging. Writing fast inference code for a complex spatial model with realistically …

Ecologic studies use data aggregated over groups rather than data on individuals. Such
studies are popular because they use existing databases and can offer large exposure …

Recent studies suggested a link between long-term exposure to air-pollution and COVID-19
mortality. However, due to their ecological design based on large spatial units, they neglect …

Supplement to “Penalising Model Component Complexity: A Principled, Practical Approach
to Constructing Priors”. The supplementary material contains the proofs of all theorems …

In recent years, disease mapping studies have become a routine application within
geographical epidemiology and are typically analysed within a Bayesian hierarchical model …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

Conditional autoregressive models are commonly used to represent spatial autocorrelation
in data relating to a set of non-overlapping areal units, which arise in a wide variety of …

Many statisticians have had the experience of fitting a linear model with uncorrelated errors,
then adding a spatially-correlated error term (random effect) and finding that the estimates of …

Abstract Nearly 30 years ago, Cavalli-Sforza et al. pioneered the use of principal component
analysis (PCA) in population genetics and used PCA to produce maps summarizing human …

OBJECTIVE Hypoglycemia is associated with serious health outcomes for patients treated
for diabetes. However, the outcome of outpatients with type 2 diabetes who have …