Work

Modeling Multivariate Spatial Dependencies Using Graphical Models

Year: 2023

Type: article

Abstract: Graphical models have witnessed significant growth and usage in spatial data science for modeling data referenced over a massive number of spatial-temporal coordinates. Much of this literature has focused on a single or relatively few spatially dependent outcomes. Recent attention has focused upon addressing modeling and inference for substantially large number of outcomes. While spatial factor models and multivariate basis expansions occupy a prominent place in this domain, this article elucidates a recent approach, graphical Gaussian Processes, that exploits the notion of conditional independence among a very large number of spatial processes to build scalable graphical models for fully model-based Bayesian analysis of multivariate spatial data.

Source: The New England Journal of Statistics in Data Science

DOI: 10.51387/23-nejsds47

Authors: Debangan Dey, Abhirup Datta, Sudipto Banerjee

Institutions: Johns Hopkins University, University of California, Los Angeles

Language: English

Topic: Spatial and Panel Data Analysis

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: hybrid