JSM2025
Back to the program
Contributed Posters

Contributed Poster Presentations: Section on Statistics and the Environment

Tue, Aug 5, 10:30 AM - 12:20 PM Room CC-Hall B Music City Center
Shirin GolchiChair
Section on Statistics and the Environment

11 Presentations

62: A Bayesian Approach to Species Distribution Modeling with INLA
10:30 AM - 12:20 PM
Co-authors: Kabiru Abubakari (Queen Mary University of London), Kabiru Abubakari (Queen Mary University of London), Silvia Liverani (Queen Mary University of London), Andrew Leitch (Queen Mary University of London), Ilia Leitch (Royal Botanical Gardens)
Presence onlyMisclassificationINLABayesianModel Averaging
63: A Smooth Transition Model Furnishing a Test for the Broken-stick Model
10:30 AM - 12:20 PM
Co-authors: Sebastian McCrimmon (Iowa State University), Wayne Fuller (Iowa State University), Sebastian McCrimmon (Iowa State University)
Change-pointSegmented regressionTransition model
64: A Zero-Inflated Weighted Distribution Mixture Model for Spatial Compositional Data in Ecology
10:30 AM - 12:20 PM
Co-authors: Jay Brown (Penn State), Ephraim Hanks (Penn State), Jay Brown (Penn State)
spatial statisticsBayesian statisticsecologyDirichlet Process prioreBird
65: Accounting for Spatial Confounding in the Spectral Domain for Multiple Exposures and Responses
10:30 AM - 12:20 PM
Co-authors: Shih-Ni Prim (North Carolina State University), Shih-Ni Prim (North Carolina State University), Yawen Guan (Colorado State University), Shu Yang (North Carolina State University, Department of Statistics), Ana Rappold (US EPA), K. Lloyd Hill (Oak Ridge Associated Universities and US EPA), Corinna Keeler (US EPA), Wei-Lun Tsai (US EPA), Brian Reich (North Carolina State University)
spatial confoundingcausal inferencetensor decomposition
66: Analyzing Regional Variations in Air Quality: Insights from Indiana, Ohio, and Kentucky (2020–2024)
10:30 AM - 12:20 PM
Co-authors: Nelum Hapuhinna (Northern Kentucky University), Nelum Hapuhinna (Northern Kentucky University)
Air QualitySpatial StatisticsGeographically Weighted RegressionMultiscale Geographically Weighted RegressionClimate changeStatistics
67: Distinguishing High/Low Exposure in Marked Spatial Point Processes Using a Bayesian Mixture Model
10:30 AM - 12:20 PM
Co-authors: Linyu Zhou (University of California, Los Angeles), Thomas Belin (University of California-Los Angeles), Linyu Zhou (University of California, Los Angeles), Honghu Liu (Department of Biostatistics, UCLA)
Bayesian Mixture ModelMarked Log‐Gaussian Cox ProcessExposure HeterogeneitySpatial Point ProcessMCMCSimulation Study
68: Heterogenous Velocity Models Help Identify Differences in the Movement of Invasive Species
10:30 AM - 12:20 PM
Co-authors: Myungsoo Yoo (University of Texas at Austin), Berkeley Ho, Clinton Leach (Colorado State University), Abigail Feuka (Colorado State University), Mevin Hooten (The University of Texas At Austin)
69: K-MEANS CLUSTERING RESOLUTION OF DIURNAL WIND PATTERN MODES AND PATTERN EXTREMA FOR NASHVILLE, TN
10:30 AM - 12:20 PM
Co-authors: Charles Fisk, Charles Fisk
K-MEANS CLUSTERING OF NORTH/SOUTH AND EAST\WEST WIND COMPONENTSDIURNAL RESULTANT WINDS PATTERNSSQURED EUCLIDEAN STATISTICAL DISTANCESK=1 CLUSTER ANALYSIS MANIPULATION
70: Land Use Regression Models for Predicting PM2.5: A Comparative Analysis from the Accra Birth Cohort
10:30 AM - 12:20 PM
Co-authors: Benjamin Abijah, Benjamin Abijah, Raphael Arko (University of Massachusetts Amherst), Raji Balasubramanian
land use regressionPM2.5air pollutionmachine learningBayesian methods
71: New Bayesian Models for the Heterogeneous Evolution of Count Variables
10:30 AM - 12:20 PM
Co-authors: Kevin Surya, John Borkowski (Montana State University), Kevin Surya, Chris Organ (Montana State University), Andrew Hoegh (Montana State University)
evolutionphylogeneticstochasticBayesian
72: On physics-informed neural networks for ecological diffusion modeling of wildlife diseases
10:30 AM - 12:20 PM
Co-authors: Juan Francisco Mandujano Reyes (University of Wisconsin - Madison), Ting Fung Ma (University of South Carolina), Juan Francisco Mandujano Reyes (University of Wisconsin - Madison), Ian McGahan (University of Wisconsin-Madison), Daniel Walsh (U.S. Geological Survey Montana Cooperative Wildlife Research Unit), Jun Zhu (University of Wisconsin - Madison)
physics-informed neural networkecologyenvironmental statisticsspatiotemporal dataecological diffusion equationdeep learning
↑