| Autor: |
Peruzzi M; Department of Forestry, Michigan State University.; Department of Statistical Science, Duke University., Banerjee S; Department of Biostatistics, UCLA Fielding School of Public Health., Finley AO; Department of Forestry, Michigan State University. |
| Jazyk: |
angličtina |
| Zdroj: |
Journal of the American Statistical Association [J Am Stat Assoc] 2022; Vol. 117 (538), pp. 969-982. Date of Electronic Publication: 2020 Nov 24. |
| DOI: |
10.1080/01621459.2020.1833889 |
| Abstrakt: |
We introduce a class of scalable Bayesian hierarchical models for the analysis of massive geostatistical datasets. The underlying idea combines ideas on high-dimensional geostatistics by partitioning the spatial domain and modeling the regions in the partition using a sparsity-inducing directed acyclic graph (DAG). We extend the model over the DAG to a well-defined spatial process, which we call the Meshed Gaussian Process (MGP). A major contribution is the development of a MGPs on tessellated domains, accompanied by a Gibbs sampler for the efficient recovery of spatial random effects. In particular, the cubic MGP (Q-MGP) can harness high-performance computing resources by executing all large-scale operations in parallel within the Gibbs sampler, improving mixing and computing time compared to sequential updating schemes. Unlike some existing models for large spatial data, a Q-MGP facilitates massive caching of expensive matrix operations, making it particularly apt in dealing with spatiotemporal remote-sensing data. We compare Q-MGPs with large synthetic and real world data against state-of-the-art methods. We also illustrate using Normalized Difference Vegetation Index (NDVI) data from the Serengeti park region to recover latent multivariate spatiotemporal random effects at millions of locations. The source code is available at github.com/mkln/meshgp. |
| Databáze: |
MEDLINE |
| Externí odkaz: |
|
