Bayesian nonstationary and nonparametric covariance estimation for large spatial data (with discussion)
In spatial statistics, it is often assumed that the spatial field of interest is stationary and its
covariance has a simple parametric form, but these assumptions are not appropriate in …
covariance has a simple parametric form, but these assumptions are not appropriate in …
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks
Spatial processes observed in various fields, such as climate and environmental science,
often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a …
often occur at large-scale and demonstrate spatial nonstationarity. However, fitting a …
Modeling and Predicting Spatio-temporal Dynamics of PM Concentrations Through Time-evolving Covariance Models
Fine particulate matter (PM $ _ {2.5} $) has become a great concern worldwide due to its
adverse health effects. PM $ _ {2.5} $ concentrations typically exhibit complex spatio …
adverse health effects. PM $ _ {2.5} $ concentrations typically exhibit complex spatio …
Assessing Spatial Stationarity and Segmenting Spatial Processes into Stationary Components
SL Tzeng, BY Chen, HC Huang - Journal of Agricultural, Biological and …, 2024 - Springer
In this research, we propose a novel technique for visualizing nonstationarity in geostatistics,
particularly when confronted with a single realization of data at irregularly spaced locations …
particularly when confronted with a single realization of data at irregularly spaced locations …
Flexible Covariance Models for Spatio-Temporal and Multivariate Spatial Random Fields
GA Qadir - 2021 - repository.kaust.edu.sa
The modeling of spatio-temporal and multivariate spatial random fields has been an
important and growing area of research due to the increasing availability of spacetime …
important and growing area of research due to the increasing availability of spacetime …