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09/08/2023
Commentary on Guyll et al. (2023): Misuse of Statistical Method Results in Highly Biased Interpretation of Forensic Evidence
Since the National Academy of Sciences released their report outlining p...
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08/23/2023
Consistency of common spatial estimators under spatial confounding
This paper addresses the asymptotic performance of popular spatial regre...
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07/21/2023
Visibility graph-based covariance functions for scalable spatial analysis in nonconvex domains
We present a new method for constructing valid covariance functions of G...
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07/07/2023
Density-on-Density Regression
In this study, a density-on-density regression model is introduced, wher...
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04/18/2023
Neural networks for geospatial data
Analysis of geospatial data has traditionally been model-based, with a m...
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03/11/2023
Direct Bayesian Regression for Distribution-valued Covariates
In this manuscript, we study the problem of scalar-on-distribution regre...
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02/27/2023
Random forests for binary geospatial data
Binary geospatial data is commonly analyzed with generalized linear mixe...
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09/15/2022
Improved fMRI-based Pain Prediction using Bayesian Group-wise Functional Registration
In recent years, neuroimaging has undergone a paradigm shift, moving awa...
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09/13/2022
On the Relationship between Graphical Gaussian Processes and Functional Gaussian Graphical Models
Multivariate functional or spatial data are commonly analysed using mult...
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03/28/2022
Gaussian Process filtering for calibration of low-cost air-pollution sensor network data
Low-cost air pollution sensors, offering hyper-local characterization of...
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12/30/2021
Approaches to spatial confounding in geostatistics
Research in the past few decades has discussed the concept of "spatial c...
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02/26/2021
Sparse Cholesky matrices in spatial statistics
Gaussian Processes (GP) is a staple in the toolkit of a spatial statisti...
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02/19/2021
Bayesian Functional Registration of fMRI Data
Functional magnetic resonance imaging (fMRI) has provided invaluable ins...
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02/04/2021
Hierarchical Multivariate Directed Acyclic Graph Auto-Regressive (MDAGAR) models for spatial diseases mapping
Disease mapping is an important statistical tool used by epidemiologists...
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09/10/2020
Graphical Gaussian Process Models for Highly Multivariate Spatial Data
For multivariate spatial (Gaussian) process models, common cross-covaria...
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07/30/2020
Random Forests for dependent data
Random forest (RF) is one of the most popular methods for estimating reg...
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04/16/2020
A Transformation-free Linear Regression for Compositional Outcomes and Predictors
Compositional data are common in many fields, both as outcomes and predi...
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01/24/2020
R package for Nearest Neighbor Gaussian Process models
This paper describes and illustrates functionality of the spNNGP R packa...
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01/15/2020
Generalized Bayesian Quantification Learning
Quantification Learning is the task of prevalence estimation for a test ...
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11/26/2019
Spatial Modeling for Correlated Cancers Using Bivariate Directed Graphs
Disease maps are an important tool in cancer epidemiology used for the a...
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10/08/2019
Percentile-Based Residuals for Model Assessment
Residuals are a key component of diagnosing model fit. The usual practic...
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10/24/2018
Local calibration of verbal autopsy algorithms
Computer-coded-verbal-autopsy (CCVA) algorithms used to generate burden-...
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02/01/2018
Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments
With continued advances in Geographic Information Systems and related co...
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01/06/2018