Random forests for spatially dependent data
Spatial linear mixed-models, consisting of a linear covariate effect and a Gaussian process
(GP) distributed spatial random effect, are widely used for analyses of geospatial data. We …
(GP) distributed spatial random effect, are widely used for analyses of geospatial data. We …
nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes
Feature selection to identify spatially variable genes or other biologically informative genes
is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose …
is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose …
Statistical field calibration of a low-cost PM2. 5 monitoring network in Baltimore
Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about
ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory …
ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory …
Nearest‐neighbor sparse Cholesky matrices in spatial statistics
A Datta - Wiley Interdisciplinary Reviews: Computational …, 2022 - Wiley Online Library
Gaussian process (GP) is a staple in the toolkit of a spatial statistician. Well‐documented
computing roadblocks in the analysis of large geospatial datasets using GPs have now …
computing roadblocks in the analysis of large geospatial datasets using GPs have now …
Evaluating spatially variable gene detection methods for spatial transcriptomics data
Background The identification of genes that vary across spatial domains in tissues and cells
is an essential step for spatial transcriptomics data analysis. Given the critical role it serves …
is an essential step for spatial transcriptomics data analysis. Given the critical role it serves …
A comparison of residential apartment rent price predictions using a large data set: Kriging versus deep neural network
H Seya, D Shiroi - Geographical Analysis, 2022 - Wiley Online Library
Despite several attempts to compare and examine the predictive accuracy of real estate
sales and rent prices between the regression‐based and neural‐network (NN)‐based …
sales and rent prices between the regression‐based and neural‐network (NN)‐based …
spNNGP R package for nearest neighbor Gaussian process models
This paper describes and illustrates functionality of the spNNGP R package. The package
provides a suite of spatial regression models for Gaussian and non-Gaussian point …
provides a suite of spatial regression models for Gaussian and non-Gaussian point …
Neural networks for geospatial data
W Zhan, A Datta - Journal of the American Statistical Association, 2024 - Taylor & Francis
Analysis of geospatial data has traditionally been model-based, with a mean model,
customarily specified as a linear regression on the covariates, and a Gaussian process …
customarily specified as a linear regression on the covariates, and a Gaussian process …
Graphical Gaussian process models for highly multivariate spatial data
For multivariate spatial Gaussian process models, customary specifications of cross-
covariance functions do not exploit relational inter-variable graphs to ensure process-level …
covariance functions do not exploit relational inter-variable graphs to ensure process-level …
A causal inference framework for spatial confounding
Recently, addressing spatial confounding has become a major topic in spatial statistics.
However, the literature has provided conflicting definitions, and many proposed definitions …
However, the literature has provided conflicting definitions, and many proposed definitions …