@article{reich_yang_guan_2022, title={Discussion on "Spatial plus : A novel approach to spatial confounding" by Dupont, Wood, and Augustin}, volume={3}, ISSN={["1541-0420"]}, DOI={10.1111/biom.13651}, abstractNote={Congratulations to the authors for this thoughtful and timely contribution to the spatial confounding literature. The intuitive nature of the method and simplicity of the estimation procedure will surely make Spatial+ popular with practitioners, and the theoretical developments are a major advance for researchers in this area. There is much to discuss! We have formatted our discussion in two sections: in Section 2 we consider the assumptions and statistical properties of Spatial+, and in Section 3 we examine how Spatial+ fits in the wider literature on spatial causal inference.}, journal={BIOMETRICS}, author={Reich, Brian J. and Yang, Shu and Guan, Yawen}, year={2022}, month={Mar} }
 @article{reich_guan_fourches_warren_sarnat_chang_2020, title={INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH}, volume={14}, ISSN={["1941-7330"]}, DOI={10.1214/20-AOAS1364}, abstractNote={Humans are concurrently exposed to chemically, structurally and toxicologically diverse chemicals. A critical challenge for environmental epidemiology is to quantify the risk of adverse health outcomes resulting from exposures to such chemical mixtures and to identify which mixture constituents may be driving etiologic associations. A variety of statistical methods have been proposed to address these critical research questions. However, they generally rely solely on measured exposure and health data available within a specific study. Advancements in understanding of the role of mixtures on human health impacts may be better achieved through the utilization of external data and knowledge from multiple disciplines with innovative statistical tools. In this paper we develop new methods for health analyses that incorporate auxiliary information about the chemicals in a mixture, such as physicochemical, structural and/or toxicological data. We expect that the constituents identified using auxiliary information will be more biologically meaningful than those identified by methods that solely utilize observed correlations between measured exposure. We develop flexible Bayesian models by specifying prior distributions for the exposures and their effects that include auxiliary information and examine this idea over a spectrum of analyses from regression to factor analysis. The methods are applied to study the effects of volatile organic compounds on emergency room visits in Atlanta. We find that including cheminformatic information about the exposure variables improves prediction and provides a more interpretable model for emergency room visits for respiratory diseases.}, number={4}, journal={ANNALS OF APPLIED STATISTICS}, author={Reich, Brian J. and Guan, Yawen and Fourches, Denis and Warren, Joshua L. and Sarnat, Stefanie E. and Chang, Howard H.}, year={2020}, month={Dec}, pages={1945–1963} }
 @article{majumder_guan_reich_o'neill_rappold_2021, title={Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire PM2.5 Concentration Forecasting}, volume={26}, ISSN={["1537-2693"]}, DOI={10.1007/s13253-020-00420-4}, abstractNote={Fine particulate matter, PM2.5, has been documented to have adverse health effects and wildland fires are a major contributor to PM2.5 air pollution in the US. Forecasters use numerical models to predict PM2.5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods.}, number={1}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Majumder, Suman and Guan, Yawen and Reich, Brian J. and O'Neill, Susan and Rappold, Ana G.}, year={2021}, month={Mar}, pages={23–44} }
 @article{guan_sampson_tucker_chang_mondal_haran_sulsky_2019, title={Computer Model Calibration Based on Image Warping Metrics: An Application for Sea Ice Deformation}, volume={24}, ISSN={["1537-2693"]}, DOI={10.1007/s13253-019-00353-7}, abstractNote={Arctic sea ice plays an important role in the global climate. Sea ice models governed by physical equations have been used to simulate the state of the ice including characteristics such as ice thickness, concentration, and motion. More recent models also attempt to capture features such as fractures or leads in the ice. These simulated features can be partially misaligned or misshapen when compared to observational data, whether due to numerical approximation or incomplete physics. In order to make realistic forecasts and improve understanding of the underlying processes, it is necessary to calibrate the numerical model to field data. Traditional calibration methods based on generalized least-square metrics are flawed for linear features such as sea ice cracks. We develop a statistical emulation and calibration framework that accounts for feature misalignment and misshapenness, which involves optimally aligning model output with observed features using cutting-edge image registration techniques. This work can also have application to other physical models which produce coherent structures. Supplementary materials accompanying this paper appear online.}, number={3}, journal={JOURNAL OF AGRICULTURAL BIOLOGICAL AND ENVIRONMENTAL STATISTICS}, author={Guan, Yawen and Sampson, Christian and Tucker, J. Derek and Chang, Won and Mondal, Anirban and Haran, Murali and Sulsky, Deborah}, year={2019}, month={Sep}, pages={444–463} }
 @article{guan_johnson_katzfuss_mannshardt_messier_reich_song_2020, title={Fine-Scale Spatiotemporal Air Pollution Analysis Using Mobile Monitors on Google Street View Vehicles}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2019.1665526}, abstractNote={Abstract People are increasingly concerned with understanding their personal environment, including possible exposure to harmful air pollutants. To make informed decisions on their day-to-day activities, they are interested in real-time information on a localized scale. Publicly available, fine-scale, high-quality air pollution measurements acquired using mobile monitors represent a paradigm shift in measurement technologies. A methodological framework utilizing these increasingly fine-scale measurements to provide real-time air pollution maps and short-term air quality forecasts on a fine-resolution spatial scale could prove to be instrumental in increasing public awareness and understanding. The Google Street View study provides a unique source of data with spatial and temporal complexities, with the potential to provide information about commuter exposure and hot spots within city streets with high traffic. We develop a computationally efficient spatiotemporal model for these data and use the model to make short-term forecasts and high-resolution maps of current air pollution levels. We also show via an experiment that mobile networks can provide more nuanced information than an equally sized fixed-location network. This modeling framework has important real-world implications in understanding citizens’ personal environments, as data production and real-time availability continue to be driven by the ongoing development and improvement of mobile measurement technologies. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.}, number={531}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Guan, Yawen and Johnson, Margaret C. and Katzfuss, Matthias and Mannshardt, Elizabeth and Messier, Kyle P. and Reich, Brian J. and Song, Joon J.}, year={2020}, month={Jul}, pages={1111–1124} }
 @article{grantham_guan_reich_borer_gross_2020, title={MIMIX: A Bayesian Mixed-Effects Model for Microbiome Data From Designed Experiments}, volume={115}, ISSN={["1537-274X"]}, DOI={10.1080/01621459.2019.1626242}, abstractNote={Abstract Recent advances in bioinformatics have made high-throughput microbiome data widely available, and new statistical tools are required to maximize the information gained from these data. For example, analysis of high-dimensional microbiome data from designed experiments remains an open area in microbiome research. Contemporary analyses work on metrics that summarize collective properties of the microbiome, but such reductions preclude inference on the fine-scale effects of environmental stimuli on individual microbial taxa. Other approaches model the proportions or counts of individual taxa as response variables in mixed models, but these methods fail to account for complex correlation patterns among microbial communities. In this article, we propose a novel Bayesian mixed-effects model that exploits cross-taxa correlations within the microbiome, a model we call microbiome mixed model (MIMIX). MIMIX offers global tests for treatment effects, local tests and estimation of treatment effects on individual taxa, quantification of the relative contribution from heterogeneous sources to microbiome variability, and identification of latent ecological subcommunities in the microbiome. MIMIX is tailored to large microbiome experiments using a combination of Bayesian factor analysis to efficiently represent dependence between taxa and Bayesian variable selection methods to achieve sparsity. We demonstrate the model using a simulation experiment and on a 2 × 2 factorial experiment of the effects of nutrient supplement and herbivore exclusion on the foliar fungal microbiome of Andropogon gerardii, a perennial bunchgrass, as part of the global Nutrient Network research initiative. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.}, number={530}, journal={JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION}, author={Grantham, Neal S. and Guan, Yawen and Reich, Brian J. and Borer, Elizabeth T. and Gross, Kevin}, year={2020}, month={Apr}, pages={599–609} }