Showing 1–20 of 21 results
/ Date/ Name
Mar 29, 2017Spatially-Dependent Multiple Testing Under Model Misspecification, with Application to Detection of Anthropogenic Influence on Extreme Climate EventsJul 25, 2023A flexible class of priors for orthonormal matrices with basis function-specific structureAug 19, 2016Nonstationary Spatial Prediction of Soil Organic Carbon: Implications for Stock Assessment Decision MakingDec 5, 2025gp2Scale: A Class of Compactly-Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data PointsAug 13, 2024Granger causal inference for climate change attributionOct 6, 2014Regression-based covariance functions for nonstationary spatial modelingJul 30, 2015Local likelihood estimation for covariance functions with spatially-varying parameters: the convoSPAT package for ROct 7, 2016Review: Nonstationary Spatial Modeling, with Emphasis on Process Convolution and Covariate-Driven ApproachesJul 7, 2023Explaining the unexplainable: leveraging extremal dependence to characterize the 2021 Pacific Northwest heatwaveMar 18, 2026Wasserstein-type Gaussian Process Regressions for Input Measurement UncertaintyNov 7, 2024Compactly-supported nonstationary kernels for computing exact Gaussian processes on big dataJul 11, 2018A probabilistic gridded product for daily precipitation extremes over the United StatesSep 18, 2023A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian ProcessesFeb 15, 2019Detected changes in precipitation extremes at their native scales derived from in situ measurementsAug 9, 2024Data-driven upper bounds and event attribution for unprecedented heatwavesDec 10, 2024Spatial scale-aware tail dependence modeling for high-dimensional spatial extremesMar 11, 2026GGMPs: Generalized Gaussian Mixture ProcessesNov 12, 2019The effect of geographic sampling on evaluation of extreme precipitation in high resolution climate modelsOct 30, 2019Bayesian inference for high-dimensional nonstationary Gaussian processesMay 18, 2022Exact Gaussian Processes for Massive Datasets via Non-Stationary Sparsity-Discovering Kernels