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Skew-Gaussian Spatiotemporal Change of Support Model

Abstract Number:

2783 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Eleanor Cain (1), Hossein Moradi Rekabdarkolaee (2), Semhar Michael (2)

Institutions:

(1) N/A, N/A, (2) South Dakota State University, N/A

Co-Author(s):

Hossein Moradi Rekabdarkolaee  
South Dakota State University
Semhar Michael  
South Dakota State University

First Author:

Eleanor Cain  
N/A

Presenting Author:

Eleanor Cain  
N/A

Abstract Text:

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.

Keywords:

Bayesian Inference|Skew-Gaussian|Gibbs sampling|Spatiotemporal | |

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Space, time and process modeling

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