ArticleBiometrics2022
Spatial factor modeling: A Bayesian matrix-normal approach for misaligned data.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- Bayesian Inference for Spatial-Temporal Non-Gaussian Data Using Predictive Stacking.Bayesian analysis · 2026Article
- Nonstationary Spatial Process Models with Spatially Varying Covariance Kernels.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- Quadratic inference with dense functional responses.Journal of multivariate analysis · 2025Article
- Gridding and Parameter Expansion for Scalable Latent Gaussian Models of Spatial Multivariate Data.Bayesian analysis · 2025Article
- Dynamic Bayesian Learning for Spatiotemporal Mechanistic Models.Journal of machine learning research : JMLR · 2025Article
- Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data.Journal of machine learning research : JMLR · 2025Article
- Finite Population Survey Sampling: An Unapologetic Bayesian Perspective.Sankhya. Series A. (2008) · 2024Article
- SPATIAL PREDICTIONS ON PHYSICALLY CONSTRAINED DOMAINS: APPLICATIONS TO ARCTIC SEA SALINITY DATA.The annals of applied statistics · 2024Article
- Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.PLoS computational biology · 2024Article
- Spatial meshing for general Bayesian multivariate models.Journal of machine learning research : JMLR · 2024Article
- Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process.Journal of the American Statistical Association · 2024Article
- Modeling Multivariate Spatial Dependencies Using Graphical Models.The New England Journal of Statistics in Data Science · 2023Article
- Article
- Fast increased fidelity samplers for approximate Bayesian Gaussian process regression.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2022Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
Abstract
Multivariate spatially oriented data sets are prevalent in the environmental and physical sciences. Scientists seek to jointly model multiple variables, each indexed by a spatial location, to capture any underlying spatial association for each variable and associations among the different dependent variables. Multivariate latent spatial process models have proved effective in driving statistical inference and rendering better predictive inference at arbitrary locations for the spatial process. High-dimensional multivariate spatial data, which are the theme of this article, refer to data sets where the number of spatial locations and the number of spatially dependent variables is very large. The field has witnessed substantial developments in scalable models for univariate spatial processes, but such methods for multivariate spatial processes, especially when the number of outcomes are moderately large, are limited in comparison. Here, we extend scalable modeling strategies for a single process to multivariate processes. We pursue Bayesian inference, which is attractive for full uncertainty quantification of the latent spatial process. Our approach exploits distribution theory for the matrix-normal distribution, which we use to construct scalable versions of a hierarchical linear model of coregionalization (LMC) and spatial factor models that deliver inference over a high-dimensional parameter space including the latent spatial process. We illustrate the computational and inferential benefits of our algorithms over competing methods using simulation studies and an analysis of a massive vegetation index data set.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.