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07/16/2023
Bivariate DeepKriging for Large-scale Spatial Interpolation of Wind Fields
High spatial resolution wind data are essential for a wide range of appl...
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06/23/2023
Semiparametric Estimation of the Shape of the Limiting Multivariate Point Cloud
We propose a model to flexibly estimate joint tail properties by exploit...
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06/20/2023
Spatio-temporal DeepKriging for Interpolation and Probabilistic Forecasting
Gaussian processes (GP) and Kriging are widely used in traditional spati...
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01/24/2023
R2D2 goes to space! A principled approach to setting prior distributions on spatial parameters
Spatially dependent data arises in many biometric applications, and Gaus...
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11/03/2022
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects
Standard causal inference characterizes treatment effect through average...
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10/26/2022
SPQR: An R Package for Semi-Parametric Density and Quantile Regression
We develop an R package SPQR that implements the semi-parametric quantil...
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10/08/2022
Matching Estimators of Causal Effects in Clustered Observational Studies with Application to Quantifying the Impact of Marine Protected Areas on Biodiversity
Marine conservation preserves fish biodiversity, protects marine and coa...
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08/05/2022
Modeling Extremal Streamflow using Deep Learning Approximations and a Flexible Spatial Process
Quantifying changes in the probability and magnitude of extreme flooding...
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08/05/2022
A Gaussian-process approximation to a spatial SIR process using moment closures and emulators
The dynamics that govern disease spread are hard to model because infect...
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04/29/2022
Distributed Inference for Spatial Extremes Modeling in High Dimensions
Extreme environmental events frequently exhibit spatial and temporal dep...
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01/28/2022
A penalized complexity prior for deep Bayesian transfer learning with application to materials informatics
A key task in the emerging field of materials informatics is to use mach...
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12/24/2021
Monitoring Deforestation Using Multivariate Bayesian Online Changepoint Detection with Outliers
Near real time change detection is important for a variety of Earth moni...
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11/21/2021
The R2D2 Prior for Generalized Linear Mixed Models
In Bayesian analysis, the selection of a prior distribution is typically...
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03/07/2021
Estimating intervention effects on infectious disease control: the effect of community mobility reduction on Coronavirus spread
Understanding the effects of interventions, such as restrictions on comm...
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02/27/2021
Instrumental variables, spatial confounding and interference
Unobserved spatial confounding variables are prevalent in environmental ...
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02/22/2021
Bayesian Non-parametric Quantile Process Regression and Estimation of Marginal Quantile Effects
Flexible estimation of multiple conditional quantiles is of interest in ...
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12/24/2020
Kryging: Geostatistical analysis of large-scale datasets using Krylov subspace methods
Analyzing massive spatial datasets using Gaussian process model poses co...
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12/22/2020
A spectral adjustment for spatial confounding
Adjusting for an unmeasured confounder is generally an intractable probl...
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08/17/2020
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting
Short-term forecasting is an important tool in understanding environment...
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07/23/2020
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction
In spatial statistics, a common objective is to predict the values of a ...
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07/06/2020
A review of spatial causal inference methods for environmental and epidemiological applications
The scientific rigor and computational methods of causal inference have ...
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06/16/2020
Multi-Model Penalized Regression
Model fitting often aims to fit a single model, assuming that the impose...
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05/25/2020
Spatially varying causal effect models
We establish causal effect models that allow for time- and spatially var...
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03/24/2020
A Bayesian semi-parametric hybrid model for spatial extremes with unknown dependence structure
The max-stable process is an asymptotically justified model for spatial ...
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03/12/2020
A spatial causal analysis of wildland fire-contributed PM2.5 using numerical model output
Wildland fire smoke contains hazardous levels of fine particulate matter...
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03/11/2020
A spatiotemporal recommendation engine for malaria control
Malaria is an infectious disease affecting a large population across the...
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02/03/2020
Constrained Bayesian Nonparametric Regression for Grain Boundary Energy Predictions
Grain boundary (GB) energy is a fundamental property that affects the fo...
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10/31/2019
Accounting for Location Measurement Error in Atomic Resolution Images of Crystalline Materials
Scanning transmission electron microscopy can directly image the atomic ...
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09/26/2019
Statistical downscaling with spatial misalignment: Application to wildland fire PM_2.5 concentration forecasting
Fine particulate matter, PM_2.5, has been documented to have adverse hea...
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09/05/2019
Multivariate spectral downscaling for PM2.5 species
Fine particulate matter (PM2.5) is a mixture of air pollutants that has ...
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06/15/2019
Probabilistic Diffusion MRI Fiber Tracking Using a Directed Acyclic Graph Auto-Regressive Model of Positive Definite Matrices
Diffusion MRI is a neuroimaging technique measuring the anatomical struc...
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05/29/2019
Mean-dependent nonstationary spatial models
Nonstationarity is a major challenge in analyzing spatial data. For exam...
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05/28/2019
Global forensic geolocation with deep neural networks
An important problem in forensic analyses is identifying the provenance ...
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04/17/2019
A comparison of statistical and machine learning methods for creating national daily maps of ambient PM_2.5 concentration
A typical problem in air pollution epidemiology is exposure assessment f...
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04/08/2019
Geostatistical Modeling of Positive Definite Matrices and Its Applications to Diffusion Tensor Imaging
Geostatistical modeling for continuous point-referenced data has been ex...
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03/28/2019
Nearest-Neighbor Neural Networks for Geostatistics
Kriging is the predominant method used for spatial prediction, but relie...
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03/18/2019
A Spatial Bayesian Semiparametric Mixture Model for Positive Definite Matrices with Applications to Diffusion Tensor Imaging
Diffusion tensor imaging (DTI) is a popular magnetic resonance imaging t...
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03/14/2019
Deep Distribution Regression
Due to their flexibility and predictive performance, machine-learning ba...
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02/25/2019
Estimating Atmospheric Motion Winds from Satellite Image Data using Space-time Drift Models
Geostationary satellites collect high-resolution weather data comprising...
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12/31/2018
A multivariate spatial skew-t process for joint modeling of extreme precipitation indexes
To study trends in extreme precipitation across US over the years 1951-2...
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12/31/2018
A semiparametric Bayesian model for spatiotemporal extremes
In this paper, we consider a Dirichlet process mixture of spatial skew-t...
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10/10/2018
Bayesian Nonparametric Policy Search with Application to Periodontal Recall Intervals
Tooth loss from periodontal disease is a major public health burden in t...
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10/08/2018
Fine-scale spatiotemporal air pollution analysis using mobile monitors on Google Street View vehicles
People are increasingly concerned with understanding their personal envi...
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08/01/2018
Exploration and inference in spatial extremes using empirical basis functions
Statistical methods for inference on spatial extremes of large datasets ...
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06/16/2018
A nonparametric spatial test to identify factors that shape a microbiome
The advent of high-throughput sequencing technologies has made data from...
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05/08/2018
Spatial shrinkage via the product independent Gaussian process prior
We study the problem of sparse signal detection on a spatial domain. We ...
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04/27/2018
Sequential Optimization in Locally Important Dimensions
Optimizing a black-box function is challenging when the underlying funct...
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11/11/2017