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BSTFA: An R Package for Efficient Bayesian Spatio-temporal Factor Analysis

Abstract Number:

1995 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Speed 

Participants:

Adam Simpson (1), Candace Berrett (2)

Institutions:

(1) N/A, N/A, (2) Brigham Young University, N/A

Co-Author:

Candace Berrett  
Brigham Young University

Speaker:

Adam Simpson  
N/A

Abstract Text:

Factor analysis methods are widely used for exploring latent characteristics of a random process. Spatio-temporal factor analysis extends this approach to account for spatial and temporal dependencies in spatio-temporal data. By modeling these dependencies, the estimated processes can be interpolated to unobserved locations. However, Bayesian spatio-temporal models are notoriously computationally burdensome, limiting their practical use. To address this, we developed the BSTFA package in R to automatically fit an efficient Bayesian spatio-temporal factor analysis model using dimension-reduced basis functions. The BSTFA package is user-friendly, computationally fast, and provides a powerful tool for modeling and interpreting spatio-temporal dependencies. We demonstrate its utility with a case study modeling PM 2.5 levels across the state of California for 25 years.

Keywords:

Bayesian modeling|R Package|Spatio-temporal data|MCMC|Latent analysis|Basis functions

Sponsors:

Section on Statistics and the Environment

Tracks:

Spatio-temporal statistics

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