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Vecchia Approximations and Optimization for Multivariate Matérn Models
Youssef Fahmy, Joseph Guinness
We describe our implementation of the multivariate Matérn model for multivariate spatial datasets, using Vecchia's approximation and a Fisher scoring optimization algorithm. We con…
Mean-dependent nonstationary spatial models
Geoffrey Colin Lee Peterson, Joseph Guinness, Adam Terando +1
Nonstationarity is a major challenge in analyzing spatial data. For example, daily precipitation measurements may have increased variability and decreased spatial smoothness in are…
Smooth Density Spatial Quantile Regression
Halley Brantley, Montserrat Fuentes, Joseph Guinness +1
We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling t…
Baseline Drift Estimation for Air Quality Data Using Quantile Trend Filtering
Halley L. Brantley, Joseph Guinness, Eric C. Chi
We address the problem of estimating smoothly varying baseline trends in time series data. This problem arises in a wide range of fields, including chemistry, macroeconomics, and m…
Nonparametric Spectral Methods for Multivariate Spatial and Spatial-Temporal Data
Joseph Guinness
We propose computationally efficient methods for estimating stationary multivariate spatial and spatial-temporal spectra from incomplete gridded data. The methods are iterative and…
Vecchia approximations of Gaussian-process predictions
Matthias Katzfuss, Joseph Guinness, Wenlong Gong +1
Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasib…