Skew-Gaussian Spatiotemporal Change of Support Model

Music City Center 

Analyzing data that is inherently spatiotemporal can be difficult when our objective becomes estimating observations on a spatial and/or temporal domain that differs from the domain of our original data. The Spatiotemporal Change of Support (STCOS) model aims to solve this problem. Often, the data used in a STCOS model is assumed to follow a Gaussian distribution. However, when presented with non-Gaussian data, this assumption is unrealistic and unreliable. This research aims to extend the STCOS model to a non-Gaussian setting. We propose a Bayesian hierarchical model and implement a Markov Chain Monte Carlo Gibbs sampler to develop a Skew-Gaussian STCOS model that accounts for skewness in the data.

Bayesian Inference

Skew-Gaussian

Gibbs sampling

Spatiotemporal 

Section on Bayesian Statistical Science