16 citations · 46 across the 16 of their papers we have counts for
17 papers
Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks
Sweta Rai, Alexis Hoffman, Soumendra Lahiri +3
The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. H…
Regridding Uncertainty for Statistical Downscaling of Solar Radiation
Maggie Bailey, Douglas Nychka, Manajit Sengupta +3
Initial steps in statistical downscaling involve being able to compare observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM output from th…
Adapting conditional simulation using circulant embedding for irregularly spaced spatial data
Maggie D. Bailey, Soutir Bandyopadhyay, Douglas W. Nychka
Computing an ensemble of random fields using conditional simulation is an ideal method for retrieving accurate estimates of a field conditioned on available data and for quantifyin…
Fast covariance parameter estimation of spatial Gaussian process models using neural networks
Florian Gerber, Douglas W. Nychka
Gaussian processes (GPs) are a popular model for spatially referenced data and allow descriptive statements, predictions at new locations, and simulation of new fields. Often a few…
Nonrigid registration using Gaussian processes and local likelihood estimation
Ashton Wiens, William Kleiber, Douglas Nychka +1
Surface registration, the task of aligning several multidimensional point sets, is a necessary task in many scientific fields. In this work, a novel statistical approach is develop…
Modeling spatial data using local likelihood estimation and a Matérn to SAR translation
Ashton Wiens, Douglas Nychka, William Kleibe
Modeling data with non-stationary covariance structure is important to represent heterogeneity in geophysical and other environmental spatial processes. In this work, we investigat…