284 citations · 399 across the 11 of their papers we have counts for
12 papers
FireGen: Quantifying Wildfire Risk by Simulation
Allyson Hineman, William Kleiber, Stephan Sain +1
Wildfire regimes in Northern California exhibit strong spatio-temporal variability, heavy-tailed fire size distributions, and sensitivity to climatic conditions. We develop a hiera…
Modeling massive highly-multivariate nonstationary spatial data with the basis graphical lasso
Mitchell Krock, William Kleiber, Dorit Hammerling +1
We propose a new modeling framework for highly-multivariate spatial processes that synthesizes ideas from recent multiscale and spectral approaches with graphical models. The basis…
Stochastic Tropical Cyclone Precipitation Field Generation
William Kleiber, Stephan Sain, Luke Madaus +1
Tropical cyclones are important drivers of coastal flooding which have severe negative public safety and economic consequences. Due to the rare occurrence of such events, high spat…
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…
Penalized basis models for very large spatial datasets
Mitchell Krock, William Kleiber, Stephen Becker
Many modern spatial models express the stochastic variation component as a basis expansion with random coefficients. Low rank models, approximate spectral decompositions, multireso…
Improving particle filter performance by smoothing observations
Gregor Robinson, Ian Grooms, William Kleiber
This article shows that increasing the observation variance at small scales can reduce the ensemble size required to avoid collapse in particle filtering of spatially-extended dyna…
Spatial Bayesian hierarchical modeling of precipitation extremes over a large domain
Cameron Bracken, Balaji Rajagopalan, Linyin Cheng +2
We propose a Bayesian hierarchical model for spatial extremes on a large domain. In the data layer a Gaussian elliptical copula having generalized extreme value (GEV) marginals is…
Rejoinder of ``Cross-Covariance Functions for Multivariate Geostatistics''
Marc G. Genton, William Kleiber
Rejoinder of ``Cross-Covariance Functions for Multivariate Geostatistics'' by Genton and Kleiber [arXiv:1507.08017].
Cross-Covariance Functions for Multivariate Geostatistics
Marc G. Genton, William Kleiber
Continuously indexed datasets with multiple variables have become ubiquitous in the geophysical, ecological, environmental and climate sciences, and pose substantial analysis chall…
Coherence for Random Fields
William Kleiber
Multivariate spatial field data are increasingly common and whose modeling typically relies on building cross-covariance functions to describe cross-process relationships. An alter…
Daily minimum and maximum temperature simulation over complex terrain
William Kleiber, Richard W. Katz, Balaji Rajagopalan
Spatiotemporal simulation of minimum and maximum temperature is a fundamental requirement for climate impact studies and hydrological or agricultural models. Particularly over regi…