1 citations · 1 across the 9 of their papers we have counts for
9 papers
An Interpretable Low-Rank State-Space Model for Multi-Horizon Simulation of Large-Scale Regional Temperature Fields
Varun Kotharkar, Michael L. Stein
We propose an interpretable low-rank state-space model for conditional simulation of daily regional temperature fields. The central statistical question is whether empirical orthog…
Stochastic weather generators for high-frequency wind vector time series
Mingshi Cui, Kevin Eng, Justin T. Greene +5
Surface winds can vary substantially from one minute to the next, so there is scope for studying its variation on this fine time scale. Restricting to the month of June to minimize…
Fixed and Increasing Domain Asymptotics for the Roughness and Scale of Isotropic Gaussian Random Fields
Varun Kotharkar, Michael L. Stein
We establish a rigorous asymptotic theory for the joint estimation of roughness and scale parameters in two-dimensional Gaussian random fields with power-law generalized covariance…
Consistent Infill Estimability of the Regression Slope Between Gaussian Random Fields Under Spatial Confounding
Abhirup Datta, Michael L. Stein
The problem of estimating the slope parameter in regression between two spatial processes under confounding by an unmeasured spatial process has received widespread attention in th…
DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting
Feng Ye, Xinxi Zhang, Michael Stein +1
To unlock access to stronger winds, the offshore wind industry is advancing towards significantly larger and taller wind turbines. This massive upscaling motivates a departure from…
Modeling spatial asymmetries in teleconnected extreme temperatures
Mitchell L. Krock, Julie Bessac, Michael L. Stein
Combining strengths from deep learning and extreme value theory can help describe complex relationships between variables where extreme events have significant impacts (e.g., envir…
Teleconnected warm and cold extremes of North American wintertime temperatures
Mitchell L. Krock, Adam H. Monahan, Michael L. Stein
Current models for spatial extremes are concerned with the joint upper (or lower) tail of the distribution at two or more locations. Such models cannot account for teleconnection p…
Fitting Matérn Smoothness Parameters Using Automatic Differentiation
Christopher J. Geoga, Oana Marin, Michel Schanen +1
The Matérn covariance function is ubiquitous in the application of Gaussian processes to spatial statistics and beyond. Perhaps the most important reason for this is that the smoot…
Flexible nonstationary spatio-temporal modeling of high-frequency monitoring data
Christopher J. Geoga, Mihai Anitescu, Michael L. Stein
Many physical datasets are generated by collections of instruments that make measurements at regular time intervals. For such regular monitoring data, we extend the framework of ha…