COVID-19 has caused a serious impact on the global economy. Effectively stimulating
consumption has become a momentous mission in responding to the impact of the …

The issue of spatial confounding between the spatial random effect and the fixed effects in
regression analyses has been identified as a concern in the statistical literature. Multiple …

Abstract Generalized Gaussian processes (GGPs) are highly flexible models that combine
latent GPs with potentially non-Gaussian likelihoods from the exponential family. GGPs can …

Background and objective: Fitting spatio-temporal models for areal data is crucial in many
fields such as cancer epidemiology. However, when data sets are very large, many issues …

This article is motivated by the problem of studying the joint effect of different chemical
exposures on human health outcomes. This is essentially a nonparametric regression …

Bayesian spatio-temporal modeling of Arctic sea ice extent. Supplementary Material. The
Supplementary Material contains a simulation study that compares the inference …

ABSTRACT The Weather Risk Attribution Forecast (WRAF) is a forecasting tool that uses
output from global climate models to make simultaneous attribution statements about …

Hierarchical spatial models are very flexible and popular for a vast array of applications in
areas such as ecology, social science, public health, and atmospheric science. It is common …

Exploring spatial patterns in the context of disease map** is a decisive approach to bring
evidence of geographical tendencies in assessing disease status and progression. In most …

The transmission rate is a central parameter in mathematical models of infectious disease.
Its pivotal role in outbreak dynamics makes estimating the current transmission rate and …