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Research Frontiers in Spatial Statistics and Geostatistics

Methods for modeling and predicting spatially correlated data using kriging, point processes, and spatial regression techniques.

Non-Euclidean Geometry in High-Dimensional Spatial Data
Causal Inference Across Spatially Dependent Observations
Real-Time Kriging for Dynamic Environmental Monitoring Networks
Spatial Point Processes in Irregular Domains
Multivariate Geostatistics Under Extreme Dependence
Machine Learning Interpretability in Spatial Predictions
Spatio-Temporal Forecasting Beyond Stationarity Assumptions
Graph-Based Spatial Inference for Complex Geometries
Uncertainty Quantification in Large-Scale Geospatial Integration
Spatial Confounding and Identifiability in Ecological Models

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