Reference

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Main References

Textbook Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2003). Hierarchical modeling and analysis for spatial data. Chapman and Hall/CRC.

Nearest Neighbor Gaussian Process (NNGP): Datta, A., Banerjee, S., Finley, A. O., & Gelfand, A. E. (2016). Hierarchical nearest-neighbor Gaussian process models for large geostatistical datasets. Journal of the American Statistical Association, 111(514), 800-812.

BRISC paper: Saha, A., & Datta, A. (2018). BRISC: bootstrap for rapid inference on spatial covariances. Stat, e184, DOI: 10.1002/sta4.184.

RFGLS paper: Saha, A., Basu, S., & Datta, A. (2023). Random forests for spatially dependent data. Journal of the American Statistical Association, 118(541), 665-683.

RFGLS software paper: Saha, A., Basu, S., & Datta, A. (2022). RandomForestsGLS: an r package for random forests for dependent data. Journal of open source software, 7(71)

RFGLS software: https://cran.r-project.org/web/packages/RandomForestsGLS/

NN-GLS paper: Zhan, W., & Datta, A. (2024). Neural networks for geospatial data. Journal of the American Statistical Association, (just-accepted), 1-21.

NN-GLS software: https://pypi.org/project/geospaNN/