Skew-Gaussian Spatiotemporal Change of Support Model

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.Acknowledgments: The research reported in this abstract was supported by South Dakota State University, AIM-AHEAD Coordinating Center, award number OTA-21-017, and was, in part, funded by the National Institutes of Health Agreement No. 1OT2OD032581. The work is solely the responsibility of the authors and does not necessarily represent the official view of AIM-AHEAD or the National Institutes of Health.