For large datasets, spatial covariances are often modeled using basis functions and covariance of a reduced dimensional latent spatial process. For skewed data, likelihood based approaches with Gaussian assumption may not lead to faithful inference. …
The slides for my Introduction to Spatial Data (geared for non-Statisticians) can be found here:
https://jelsema.github.io/presentations/2019-intro-spatial/intro_spatial.html#1
Large spatial datasets are typically modelled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating the locations of knots assume their number is known a …
In environmental studies, the datasets exhibiting non-Gaussian properties, such as heavier or lighter tails and multimodality, are very common. The research on dealing with such datasets in reduced rank perspectives is very limited. In this chapter, …
We analyze data on the geochemical make-up of coal samples throughout the state of Illinois. The goal is to estimate the geochemical properties at unobserved locations over a specified region. Multivariate spatial modeling requires characterization …