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08/25/2022
Teleconnected warm and cold extremes of North American wintertime temperatures
Current models for spatial extremes are concerned with the joint upper (...
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08/14/2022
A Scalable Method to Exploit Screening in Gaussian Process Models with Noise
A common approach to approximating Gaussian log-likelihoods at scale exp...
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06/10/2022
Scalable Computations for Nonstationary Gaussian Processes
Nonstationary Gaussian process models can capture complex spatially vary...
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03/10/2022
Vecchia Likelihood Approximation for Accurate and Fast Inference in Intractable Spatial Extremes Models
Max-stable processes are the most popular models for high-impact spatial...
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01/01/2022
Fitting Matérn Smoothness Parameters Using Automatic Differentiation
The Matérn covariance function is ubiquitous in the application of Gauss...
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10/19/2021
Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails
In traditional extreme value analysis, the bulk of the data is ignored, ...
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07/29/2021
Neural Networks for Parameter Estimation in Intractable Models
We propose to use deep learning to estimate parameters in statistical mo...
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07/22/2020
Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data
Many physical datasets are generated by collections of instruments that ...
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08/09/2018
Scalable Gaussian Process Computations Using Hierarchical Matrices
We present a kernel-independent method that applies hierarchical matrice...
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11/16/2017
Linear-Cost Covariance Functions for Gaussian Random Fields
Gaussian random fields (GRF) are a fundamental stochastic model for spat...
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11/01/2017