3 papers
stat.ME2025
A Dimension-Reduced Multivariate Spatial Model for Extreme Events: Balancing Flexibility and Scalability
Remy MacDonald, Benjamin Seiyon Lee, John Foley +1
Modeling extreme precipitation and temperature is vital for understanding the impacts of climate change, as hazards like intense rainfall and record-breaking temperatures can resul…
stat.ME2024
A Scalable Variational Bayes Approach to Fit High-dimensional Spatial Generalized Linear Mixed Models
Jin Hyung Lee, Ben Seiyon Lee
Gaussian and discrete non-Gaussian spatial datasets are common across fields like public health, ecology, geosciences, and social sciences. Bayesian spatial generalized linear mixe…
stat.ME2022
Flexible Basis Representations for Modeling Large Non-Gaussian Spatial Data
Remy MacDonald, Benjamin Seiyon Lee
Nonstationary and non-Gaussian spatial data are common in various fields, including ecology (e.g., counts of animal species), epidemiology (e.g., disease incidence counts in suscep…