189 citations · 522 across the 27 of their papers we have counts for
31 papers
Asymptotic properties of Vecchia approximation for Gaussian processes
Myeongjong Kang, Florian Schäfer, Joseph Guinness +1
Vecchia approximation has been widely used to accurately scale Gaussian-process (GP) inference to large datasets, by expressing the joint density as a product of conditional densit…
Sparse inverse Cholesky factorization of dense kernel matrices by greedy conditional selection
Stephen Huan, Joseph Guinness, Matthias Katzfuss +2
Dense kernel matrices resulting from pairwise evaluations of a kernel function arise naturally in machine learning and statistics. Previous work in constructing sparse approximate…
Vecchia Approximations and Optimization for Multivariate Matérn Models
Youssef Fahmy, Joseph Guinness
We describe our implementation of the multivariate Matérn model for multivariate spatial datasets, using Vecchia's approximation and a Fisher scoring optimization algorithm. We con…
A Gaussian-process approximation to a spatial SIR process using moment closures and emulators
Parker Trostle, Joseph Guinness, Brian J. Reich
The dynamics that govern disease spread are hard to model because infections are functions of both the underlying pathogen as well as human or animal behavior. This challenge is in…
Comparison of CYGNSS and Jason-3 Wind Speed Measurements via Gaussian Processes
William Bekerman, Joseph Guinness
Wind is a critical component of the Earth system and has unmistakable impacts on everyday life. The CYGNSS satellite mission improves observational coverage of ocean winds via a fl…
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Jian Cao, Joseph Guinness, Marc G. Genton +1
Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty. In many applications, the number of responses and covari…
Log-Gaussian Cox Process Modeling of Large Spatial Lightning Data using Spectral and Laplace Approximations
Megan L. Gelsinger, Maryclare Griffin, David S. Matteson +1
Lightning is a destructive and highly visible product of severe storms, yet there is still much to be learned about the conditions under which lightning is most likely to occur. Th…
Ordered conditional approximation of Potts models
Anirban Chakraborty, Matthias Katzfuss, Joseph Guinness
Potts models, which can be used to analyze dependent observations on a lattice, have seen widespread application in a variety of areas, including statistical mechanics, neuroscienc…
Scaled Vecchia approximation for fast computer-model emulation
Matthias Katzfuss, Joseph Guinness, Earl Lawrence
Many scientific phenomena are studied using computer experiments consisting of multiple runs of a computer model while varying the input settings. Gaussian processes (GPs) are a po…
An Observational Study of the Effect of Nike Vaporfly Shoes on Marathon Performance
Joseph Guinness, Debasmita Bhattacharya, Jenny Chen +2
We collected marathon performance data from a systematic sample of elite and sub-elite athletes over the period 2015 to 2019, then searched the internet for publicly-available phot…
Inverses of Matern Covariances on Grids
Joseph Guinness
We conduct a study of the aliased spectral densities of Matérn covariance functions on a regular grid of points, providing clarity on the properties of a popular approximation base…
Mean-dependent nonstationary spatial models
Geoffrey Colin Lee Peterson, Joseph Guinness, Adam Terando +1
Nonstationarity is a major challenge in analyzing spatial data. For example, daily precipitation measurements may have increased variability and decreased spatial smoothness in are…
Gaussian Process Learning via Fisher Scoring of Vecchia's Approximation
Joseph Guinness
We derive a single pass algorithm for computing the gradient and Fisher information of Vecchia's Gaussian process loglikelihood approximation, which provides a computationally effi…
Smooth Density Spatial Quantile Regression
Halley Brantley, Montserrat Fuentes, Joseph Guinness +1
We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling t…
Baseline Drift Estimation for Air Quality Data Using Quantile Trend Filtering
Halley L. Brantley, Joseph Guinness, Eric C. Chi
We address the problem of estimating smoothly varying baseline trends in time series data. This problem arises in a wide range of fields, including chemistry, macroeconomics, and m…
Estimating Atmospheric Motion Winds from Satellite Image Data using Space-time Drift Models
Indranil Sahoo, Joseph Guinness, Brian J. Reich
Geostationary satellites collect high-resolution weather data comprising a series of images which can be used to estimate wind speed and direction at different altitudes. The Deriv…
Nonparametric Spectral Methods for Multivariate Spatial and Spatial-Temporal Data
Joseph Guinness
We propose computationally efficient methods for estimating stationary multivariate spatial and spatial-temporal spectra from incomplete gridded data. The methods are iterative and…
Non-Stationary Covariance Estimation using the Stochastic Score Approximation for Large Spatial Data
Amanda Muyskens, Joseph Guinness, Montserrat Fuentes
We introduce computational methods that allow for effective estimation of a flexible, parametric non-stationary spatial model when the field size is too large to compute the multiv…
Multivariate Spatial-Temporal Variable Selection with Applications to Seasonal Tropical Cyclone Modeling
Marcela Alfaro Córdoba, Montserrat Fuentes, Joseph Guinness +1
Tropical cyclone and sea surface temperature data have been used in several studies to forecast the total number of hurricanes in the Atlantic Basin. Sea surface temperature (SST)…
Vecchia approximations of Gaussian-process predictions
Matthias Katzfuss, Joseph Guinness, Wenlong Gong +1
Gaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasib…
Spatial shrinkage via the product independent Gaussian process prior
Arkaprava Roy, Brian J. Reich, Joseph Guinness +2
We study the problem of sparse signal detection on a spatial domain. We propose a novel approach to model continuous signals that are sparse and piecewise smooth as product of inde…
A Test for Isotropy on a Sphere using Spherical Harmonic Functions
Indranil Sahoo, Joseph Guinness, Brian J. Reich
Analysis of geostatistical data is often based on the assumption that the spatial random field is isotropic. This assumption, if erroneous, can adversely affect model predictions a…
Spectral Density Estimation for Random Fields via Periodic Embeddings
Joseph Guinness
We introduce methods for estimating the spectral density of a random field on a -dimensional lattice from incomplete gridded data. Data are iteratively imputed onto an expanded…
A Case Study Competition Among Methods for Analyzing Large Spatial Data
Matthew J. Heaton, Abhirup Datta, Andrew Finley +10
The Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationall…