3 papers
stat.ME2026
Modeling Large Nonstationary Spatial Data with the Full-Scale Basis Graphical Lasso
Matthew LeDuc, William Kleiber, Tomoko Matsuo
We propose a new approach for the modeling large datasets of nonstationary spatial processes that combines a latent low rank process and a sparse covariance model. The low rank com…
stat.CO2026
PoissonRatioUQ: An R package for band ratio uncertainty quantification
Matthew LeDuc, Tomoko Matsuo
We introduce an R package for Bayesian modeling and uncertainty quantification for problems involving count ratios. The modeling relies on the assumption that the quantity of inter…
stat.CO2026
The Continuous Rank Probability Score of a Generalized Beta-Prime Distribution and Some Special Cases
Matthew LeDuc
This working paper describes new results in derivations of the Continuous Ranked Probability Score of a generalized beta-prime distribution and several special cases, such as the D…