Among the many uses of hierarchical modeling, their application to the statistical analysis of
spatial and spatio-temporal data from areas such as epidemiology And environmental …

This short paper is centered on hierarchical modeling for problems in spatial and spatio-
temporal statistics. It draws its motivation from the interdisciplinary research work of the …

Understanding spatial statistics requires tools from applied and mathematical statistics,
linear model theory, regression, time series, and stochastic processes. It also requires a …

Spatio-temporal statistical models are increasingly being used across a wide variety of
scientific disciplines to describe and predict spatially-explicit processes that evolve over …

Spatial epidemiology is the description and analysis of the geographical distribution of
disease. It is more important now than ever, with modern threats such as bio-terrorism …

Spatial and spatiotemporal processes in the physical, environmental, and biological
sciences often exhibit complicated and diverse patterns across different space–time scales …

With the ready availability of spatial databases and geographical information system
software, statisticians are increasingly encountering multivariate modelling settings featuring …

Spatio‐temporal statistical models are increasingly being used across a wide variety of
scientific disciplines to describe and predict spatially explicit processes that evolve over …

Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal
processes, bridging classic ideas with modern hierarchical statistical modeling concepts and …

We propose a hierarchical model for multivariate spatial modeling and prediction under
which one specifies a joint distribution for a multivariate spatial process indirectly through …